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Sample records for cognitive neural system

  1. Structured cognition and neural systems: from rats to language.

    NARCIS (Netherlands)

    Battaglia, F.P.; Borensztajn, G.; Bod, R.

    2012-01-01

    Much of animal and human cognition is compositional in nature: higher order, complex representations are formed by (rule-governed) combination of more primitive representations. We review here some of the evidence for compositionality in perception and memory, motivating an approach that takes ideas

  2. Overlapping neural systems represent cognitive effort and reward anticipation.

    Science.gov (United States)

    Vassena, Eliana; Silvetti, Massimo; Boehler, Carsten N; Achten, Eric; Fias, Wim; Verguts, Tom

    2014-01-01

    Anticipating a potential benefit and how difficult it will be to obtain it are valuable skills in a constantly changing environment. In the human brain, the anticipation of reward is encoded by the Anterior Cingulate Cortex (ACC) and Striatum. Naturally, potential rewards have an incentive quality, resulting in a motivational effect improving performance. Recently it has been proposed that an upcoming task requiring effort induces a similar anticipation mechanism as reward, relying on the same cortico-limbic network. However, this overlapping anticipatory activity for reward and effort has only been investigated in a perceptual task. Whether this generalizes to high-level cognitive tasks remains to be investigated. To this end, an fMRI experiment was designed to investigate anticipation of reward and effort in cognitive tasks. A mental arithmetic task was implemented, manipulating effort (difficulty), reward, and delay in reward delivery to control for temporal confounds. The goal was to test for the motivational effect induced by the expectation of bigger reward and higher effort. The results showed that the activation elicited by an upcoming difficult task overlapped with higher reward prospect in the ACC and in the striatum, thus highlighting a pivotal role of this circuit in sustaining motivated behavior.

  3. 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.

  4. Neural systems for cognitive reappraisal in children and adolescents with autism spectrum disorder

    Directory of Open Access Journals (Sweden)

    Naomi B. Pitskel

    2014-10-01

    Full Text Available Despite substantial clinical and anecdotal evidence for emotion dysregulation in individuals with autism spectrum disorder (ASD, little is known about the neural substrates underlying this phenomenon. We sought to explore neural mechanisms for cognitive reappraisal in children and adolescents with ASD using functional magnetic resonance imaging (fMRI. We studied 16 youth with ASD and 15 age- and IQ-matched typically developing (TD comparison youth. Participants were instructed in the use of cognitive reappraisal strategies to increase and decrease their emotional responses to disgusting images. Participants in both groups displayed distinct patterns of brain activity for increasing versus decreasing their emotions. TD participants showed downregulation of bilateral insula and left amygdala on decrease trials, whereas ASD participants showed no modulation of insula and upregulation of left amygdala. Furthermore, TD youth exhibited increased functional connectivity between amygdala and ventrolateral prefrontal cortex compared to ASD participants when downregulating disgust, as well as decreased functional connectivity between amygdala and orbitofrontal cortex. These findings have important implications for our understanding of emotion dysregulation and its treatment in ASD. In particular, the relative lack of prefrontal-amygdala connectivity provides a potential target for treatment-related outcome measurements.

  5. Alterations in neural systems mediating cognitive flexibility and inhibition in mood disorders.

    Science.gov (United States)

    Piguet, Camille; Cojan, Yann; Sterpenich, Virginie; Desseilles, Martin; Bertschy, Gilles; Vuilleumier, Patrik

    2016-04-01

    Impairment in mental flexibility may be a key component contributing to cardinal cognitive symptoms among mood disorders patients, particularly thought control disorders. Impaired ability to switch from one thought to another might reflect difficulties in either generating new mental states, inhibiting previous states, or both. However, the neural underpinnings of impaired cognitive flexibility in mood disorders remain largely unresolved. We compared a group of mood disorders patients (n = 29) and a group of matched healthy subjects (n = 32) on a novel task-switching paradigm involving happy and sad faces, that allowed us to separate generation of a new mental set (Switch Cost) and inhibition of the previous set during switching (Inhibition Cost), using fMRI. Behavioral data showed a larger Switch Cost in patients relative to controls, but the average Inhibition Cost did not differ between groups. At the neural level, a main effect of group was found with stronger activation of the subgenual cingulate cortex in patients. The larger Switch Cost in patients was reflected by a stronger recruitment of brain regions involved in attention and executive control, including the left intraparietal sulcus, precuneus, left inferior fontal gyrus, and right anterior cingulate. Critically, activity in the subgenual cingulate cortex was not downregulated by inhibition in patients relative to controls. In conclusion, mood disorder patients have exaggerated Switch Cost relative to controls, and this deficit in cognitive flexibility is associated with increased activation of the fronto-parietal attention networks, combined with impaired modulation of the subgenual cingulate cortex when inhibition of previous mental states is needed. © 2016 Wiley Periodicals, Inc.

  6. Cognitive Systems

    DEFF Research Database (Denmark)

    The tutorial will discuss the definition of cognitive systems as the possibilities to extend the current systems engineering paradigm in order to perceive, learn, reason and interact robustly in open-ended changing environments. I will also address cognitive systems in a historical perspective...... to be modeled within a limited set of predefined specifications. There will inevitably be a need for robust decisions and behaviors in novel situations that include handling of conflicts and ambiguities based on the capability and knowledge of the artificial cognitive system. Further, there is a need...... in cognitive systems include e.g. personalized information systems, sensor network systems, social dynamics system and Web2.0, and cognitive components analysis. I will use example from our own research and link to other research activities....

  7. Two cognitive and neural systems for endogenous and exogenous spatial attention.

    Science.gov (United States)

    Chica, Ana B; Bartolomeo, Paolo; Lupiáñez, Juan

    2013-01-15

    Orienting of spatial attention is a family of phylogenetically old mechanisms developed to select information for further processing. Information can be selected via top-down or endogenous mechanisms, depending on the goals of the observers or on the task at hand. Moreover, salient and potentially dangerous events also attract spatial attention via bottom-up or exogenous mechanisms, allowing a rapid and efficient reaction to unexpected but important events. Fronto-parietal brain networks have been demonstrated to play an important role in supporting spatial attentional orienting, although there is no consensus on whether there is a single attentional system supporting both endogenous and exogenous attention, or two anatomical and functionally different attentional systems. In the present paper we review behavioral evidence emphasizing the differential characteristics of both systems, as well as their possible interactions for the control of the final orienting response. Behavioral studies reporting qualitative differences between the effects of both systems as well as double dissociations of the effects of endogenous and exogenous attention on information processing, suggest that they constitute two independent attentional systems, rather than a single one. Recent models of attentional orienting in humans have put forward the hypothesis of a dorsal fronto-parietal network for orienting spatial attention, and a more ventral fronto-parietal network for detecting unexpected but behaviorally relevant events. Non-invasive neurostimulation techniques, as well as neuropsychological data, suggest that endogenous and exogenous attention are implemented in overlapping, although partially segregated, brain circuits. Although more research is needed in order to refine our anatomical and functional knowledge of the brain circuits underlying spatial attention, we conclude that endogenous and exogenous spatial orienting constitute two independent attentional systems, with

  8. Neural systems supporting cognitive-affective interactions in adolescence: The role of puberty and implications for affective disorders

    Directory of Open Access Journals (Sweden)

    Cecile D. Ladouceur

    2012-08-01

    Full Text Available Evidence from longitudinal studies suggests that adolescence may represent a period of vulnerability that, in the context of adverse events, could contribute to developmental trajectories toward behavioral and emotional health problems, including affective disorders. Adolescence is also a sensitive period for the development of neural systems supporting cognitive-affective processes, which have been implicated in the pathophysiology of affective disorders such as anxiety and mood disorders. In particular, the onset of puberty brings about a cascade of physical, hormonal, psychological, and social changes that contribute in complex ways to the development of these systems. This article provides a brief overview of neuroimaging research pertaining to the development of cognitive-affective processes in adolescence. It also includes a brief review of evidence from animal and human neuroimaging studies suggesting that sex steroids influence the connectivity between prefrontal cortical and subcortical limbic regions in ways that contribute to increased reactivity to emotionally salient stimuli. We integrate these findings in the context of a developmental affective neuroscience framework suggesting that the impact of rising levels of sex steroids during puberty on fronto-limbic connectivity may be even greater in the context of protracted development of prefrontal cortical regions in adolescence. We conclude by discussing the implications of these findings for future research aimed at identifying neurodevelopmental markers of risk for future onset of affective disorders.

  9. 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.

  10. Deciphering the Cognitive and Neural Mechanisms Underlying ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

  11. 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...

  12. COGNITIVE LEARNING OF INTELLIGENCE SYSTEMS USING NEURAL NETWORKS: EVIDENCE FROM THE AUSTRALIAN CAPITAL MARKETS

    Directory of Open Access Journals (Sweden)

    Joachim Tan

    2002-01-01

    Full Text Available Artificial neural networks (ANNs allow users to improve forecasts through pattern recognition. The purpose of this paper is to validate ANNs as a detection tool in four financial markets. This study investigates whether market inefficiencies exist using ANN as a model. It also investigates whether additional publicly available information can provide investors with a trading advantage. In finance, any forecasting advantage obtained through the use of publicly available information albeit internal or/and external market factors suggest inefficiencies in the financial markets. In this paper, we explore the efficiency of the United States, Japan, Hong Kong and Australia. In Australia, using the ASX 200 index, we demonstrate how the inclusion of external information to our ANN improves our forecasting. Our results show accounting for external market signals significantly improves forecasts of the ASX200 index by an additional 10 percent. This suggests the inclusion of publicly available information from other markets, can improve predictions and returns for investors.

  13. Does bilingualism contribute to cognitive reserve? Cognitive and neural perspectives.

    Science.gov (United States)

    Guzmán-Vélez, Edmarie; Tranel, Daniel

    2015-01-01

    Cognitive reserve refers to how individuals actively utilize neural resources to cope with neuropathology to maintain cognitive functioning. The present review aims to critically examine the literature addressing the relationship between bilingualism and cognitive reserve to elucidate whether bilingualism delays the onset of cognitive and behavioral manifestations of dementia. Potential neural mechanisms behind this relationship are discussed. PubMed and PsycINFO databases were searched (through January 2014) for original research articles in English or Spanish languages. The following search strings were used as keywords for study retrieval: "bilingual AND reserve," "reserve AND neural mechanisms," and "reserve AND multilingualism." Growing scientific evidence suggests that lifelong bilingualism contributes to cognitive reserve and delays the onset of Alzheimer's disease symptoms, allowing bilingual individuals affected by Alzheimer's disease to live an independent and richer life for a longer time than their monolingual counterparts. Lifelong bilingualism is related to more efficient use of brain resources that help individuals maintain cognitive functioning in the presence of neuropathology. We propose multiple putative neural mechanisms through which lifelong bilinguals cope with neuropathology. The roles of immigration status, education, age of onset, proficiency, and frequency of language use on the relationship between cognitive reserve and bilingualism are considered. Implications of these results for preventive practices and future research are discussed. PsycINFO Database Record (c) 2015 APA, all rights reserved.

  14. Does Bilingualism Contribute to Cognitive Reserve? Cognitive and Neural Perspectives

    Science.gov (United States)

    Guzmán-Vélez, Edmarie; Tranel, Daniel

    2015-01-01

    Objective Cognitive reserve refers to how individuals actively utilize neural resources to cope with neuropathology in order to maintain cognitive functioning. The present review aims to critically examine the literature addressing the relationship between bilingualism and cognitive reserve in order to elucidate whether bilingualism delays the onset of cognitive and behavioral manifestations of dementia. Potential neural mechanisms behind this relationship are discussed. Method Pubmed and PsychINFO databases were searched (through January 2014) for original research articles in English or Spanish languages. The following search strings were employed as keywords for study retrieval: ‘bilingual AND reserve’, ‘reserve AND neural mechanisms’, and ‘reserve AND multilingualism’. Results Growing scientific evidence suggests that lifelong bilingualism contributes to cognitive reserve and delays the onset of Alzheimer's disease symptoms, allowing bilingual individuals affected by Alzheimer's disease to live an independent and richer life for a longer time than their monolingual counterparts. Lifelong bilingualism is related to more efficient use of brain resources that help individuals maintain cognitive functioning in the presence of neuropathology. We propose multiple putative neural mechanisms through which lifelong bilinguals cope with neuropathology. The roles of immigration status, education, age of onset, proficiency and frequency of language use on the relationship between cognitive reserve and bilingualism are considered. Conclusions Implications of these results for preventive practices and future research are discussed. PMID:24933492

  15. 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.

  16. Uncovering the neuroanatomical correlates of cognitive, affective and conative theory of mind in paediatric traumatic brain injury: a neural systems perspective.

    Science.gov (United States)

    Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Hearps, Stephen J; Beauchamp, Miriam H; Yeates, Keith O; Anderson, Vicki A

    2017-09-01

    Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure-function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems-the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are

  17. Psychopathy: cognitive and neural dysfunction.

    Science.gov (United States)

    R Blair, R James

    2013-06-01

    Psychopathy is a developmental disorder marked by emotional deficits and an increased risk for antisocial behavior. It is not equivalent to the diagnosis Antisocial Personality Disorder, which concentrates only on the increased risk for antisocial behavior and not a specific cause-ie, the reduced empathy and guilt that constitutes the emotional deficit. The current review considers data from adults with psychopathy with respect to the main cognitive accounts of the disorder that stress either a primary attention deficit or a primary emotion deficit. In addition, the current review considers data regarding the neurobiology of this disorder. Dysfunction within the amygdala's role in reinforcement learning and the role of ventromedial frontal cortex in the representation of reinforcement value is stressed. Data is also presented indicating potential difficulties within parts of temporal and posterior cingulate cortex. Suggestions are made with respect to why these deficits lead to the development of the disorder.

  18. Psychopathy: cognitive and neural dysfunction

    Science.gov (United States)

    R. Blair, R. James

    2013-01-01

    Psychopathy is a developmental disorder marked by emotional deficits and an increased risk for antisocial behavior. It is not equivalent to the diagnosis Antisocial Personality Disorder, which concentrates only on the increased risk for antisocial behavior and not a specific cause—ie, the reduced empathy and guilt that constitutes the emotional deficit. The current review considers data from adults with psychopathy with respect to the main cognitive accounts of the disorder that stress either a primary attention deficit or a primary emotion deficit. In addition, the current review considers data regarding the neurobiology of this disorder. Dysfunction within the amygdala's role in reinforcement learning and the role of ventromedial frontal cortex in the representation of reinforcement value is stressed. Data is also presented indicating potential difficulties within parts of temporal and posterior cingulate cortex. Suggestions are made with respect to why these deficits lead to the development of the disorder. PMID:24174892

  19. Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

    Science.gov (United States)

    Bast, Tobias; Pezze, Marie; McGarrity, Stephanie

    2017-10-01

    We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition

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

    Science.gov (United States)

    Prather, Richard W.

    2012-01-01

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

  1. 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.

  2. 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.

  3. 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

  4. Neural computation and the computational theory of cognition.

    Science.gov (United States)

    Piccinini, Gualtiero; Bahar, Sonya

    2013-04-01

    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. Copyright © 2012 Cognitive Science Society, Inc.

  5. Neurally dissociable cognitive components of reading deficits in subacute stroke.

    Science.gov (United States)

    Boukrina, Olga; Barrett, A M; Alexander, Edward J; Yao, Bing; Graves, William W

    2015-01-01

    According to cognitive models of reading, words are processed by interacting orthographic (spelling), phonological (sound), and semantic (meaning) information. Despite extensive study of the neural basis of reading in healthy participants, little group data exist on patients with reading deficits from focal brain damage pointing to critical neural systems for reading. Here, we report on one such study. We have performed neuropsychological testing and magnetic resonance imaging on 11 patients with left-hemisphere stroke (picture or word choices to a target based on meaning), phonology (matching word choices to a target based on rhyming), and orthography (a two-alternative forced choice of the most plausible non-word). They also read aloud pseudowords and words with high or low levels of usage frequency, imageability, and spelling-sound consistency. As predicted by the cognitive model, when averaged across patients, the influence of semantics was most salient for low-frequency, low-consistency words, when phonological decoding is especially difficult. Qualitative subtraction analyses revealed lesion sites specific to phonological processing. These areas were consistent with those shown previously to activate for phonology in healthy participants, including supramarginal, posterior superior temporal, middle temporal, inferior frontal gyri, and underlying white matter. Notable divergence between this analysis and previous functional imaging is the association of lesions in the mid-fusiform gyrus and anterior temporal lobe with phonological reading deficits. This study represents progress toward identifying brain lesion-deficit relationships in the cognitive components of reading. Such correspondences are expected to help not only better understand the neural mechanisms of reading, but may also help tailor reading therapy to individual neurocognitive deficit profiles.

  6. Infant joint attention, neural networks and social cognition.

    Science.gov (United States)

    Mundy, Peter; Jarrold, William

    2010-01-01

    Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). In this paper we argue that a neural network approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one's own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one's own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. We also propose that with development, joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. Copyright © 2010 Elsevier Ltd. All rights reserved.

  7. The specialization of function: cognitive and neural perspectives.

    Science.gov (United States)

    Mahon, Bradford Z; Cantlon, Jessica F

    2011-05-01

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

  8. Acquiring neural signals for developing a perception and cognition model

    Science.gov (United States)

    Li, Wei; Li, Yunyi; Chen, Genshe; Shen, Dan; Blasch, Erik; Pham, Khanh; Lynch, Robert

    2012-06-01

    The understanding of how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant, or of unknown relevance. Recent neurological science research in human perception, and in information science regarding contextbased modeling, provides us with a theoretical basis for using a bottom-up approach for automating the management of large amounts of information in ways directly useful for human operators. However, integration of human intelligence into a game theoretic framework for dynamic and adaptive decision support needs a perception and cognition model. For the purpose of cognitive modeling, we present a brain-computer-interface (BCI) based humanoid robot system to acquire brainwaves during human mental activities of imagining a humanoid robot-walking behavior. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model. The BCI system consists of a data acquisition unit with an electroencephalograph (EEG), a humanoid robot, and a charge couple CCD camera. An EEG electrode cup acquires brainwaves from the skin surface on scalp. The humanoid robot has 20 degrees of freedom (DOFs); 12 DOFs located on hips, knees, and ankles for humanoid robot walking, 6 DOFs on shoulders and arms for arms motion, and 2 DOFs for head yaw and pitch motion. The CCD camera takes video clips of the human subject's hand postures to identify mental activities that are correlated to the robot-walking behaviors. We use the neural signals to investigate relationships between complex humanoid robot behaviors and human mental activities for developing the perception and cognition model.

  9. Neural and cognitive correlates of social cognition. Findings from neuropsychological and neuroimaging studies

    International Nuclear Information System (INIS)

    Kobayakawa, Mutsutaka; Kawamura, Mitsuru

    2011-01-01

    Social cognition includes various components of information processing related to communication with other individuals. In this review, we have discussed 3 components of social cognitive function: face recognition, empathy, and decision making. Our social behavior involves recognition based on facial features and also involves empathizing with others; while making decisions, it is important to consider the social consequences of the course of action followed. Face recognition is divided into 2 routes for information processing: a route responsible for overt recognition of the face's identity and a route for emotional and orienting responses based on the face's personal affective significance. Two systems are possibly involved in empathy: a basic emotional contagion 'mirroring' system and a more advanced 'theory of mind' system that considers the cognitive perspective. Decision making is mediated by a widespread system that includes several cortical and subcortical components. Numerous lesion and neuroimaging studies have contributed to clarifying the neural correlates of social cognitive function, and greater information can be obtained on social cognitive function by combining these 2 approaches. (author)

  10. [Neural and cognitive correlates of social cognition: findings on neuropsychological and neuroimaging studies].

    Science.gov (United States)

    Kobayakawa, Mutsutaka; Kawamura, Mitsuru

    2011-12-01

    Social cognition includes various components of information processing related to communication with other individuals. In this review, we have discussed 3 components of social cognitive function: face recognition, empathy, and decision making. Our social behavior involves recognition based on facial features and also involves empathizing with others; while making decisions, it is important to consider the social consequences of the course of action followed. Face recognition is divided into 2 routes for information processing: a route responsible for overt recognition of the face's identity and a route for emotional and orienting responses based on the face's personal affective significance. Two systems are possibly involved in empathy: a basic emotional contagion "mirroring" system and a more advanced "theory of mind" system that considers the cognitive perspective. Decision making is mediated by a widespread system that includes several cortical and subcortical components. Numerous lesion and neuroimaging studies have contributed to clarifying the neural correlates of social cognitive function, and greater information can be obtained on social cognitive function by combining these 2 approaches.

  11. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  12. Neural activity predicts attitude change in cognitive dissonance.

    Science.gov (United States)

    van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

    2009-11-01

    When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

  13. Potential of Cognitive Computing and Cognitive Systems

    Science.gov (United States)

    Noor, Ahmed K.

    2015-01-01

    Cognitive computing and cognitive technologies are game changers for future engineering systems, as well as for engineering practice and training. They are major drivers for knowledge automation work, and the creation of cognitive products with higher levels of intelligence than current smart products. This paper gives a brief review of cognitive computing and some of the cognitive engineering systems activities. The potential of cognitive technologies is outlined, along with a brief description of future cognitive environments, incorporating cognitive assistants - specialized proactive intelligent software agents designed to follow and interact with humans and other cognitive assistants across the environments. The cognitive assistants engage, individually or collectively, with humans through a combination of adaptive multimodal interfaces, and advanced visualization and navigation techniques. The realization of future cognitive environments requires the development of a cognitive innovation ecosystem for the engineering workforce. The continuously expanding major components of the ecosystem include integrated knowledge discovery and exploitation facilities (incorporating predictive and prescriptive big data analytics); novel cognitive modeling and visual simulation facilities; cognitive multimodal interfaces; and cognitive mobile and wearable devices. The ecosystem will provide timely, engaging, personalized / collaborative, learning and effective decision making. It will stimulate creativity and innovation, and prepare the participants to work in future cognitive enterprises and develop new cognitive products of increasing complexity. http://www.aee.odu.edu/cognitivecomp

  14. Neural processes underlying cultural differences in cognitive persistence.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Lin, Lynda C

    2017-08-01

    Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

  15. Neural Plastic Effects of Cognitive Training on Aging Brain

    Directory of Open Access Journals (Sweden)

    Natalie T. Y. Leung

    2015-01-01

    Full Text Available Increasing research has evidenced that our brain retains a capacity to change in response to experience until late adulthood. This implies that cognitive training can possibly ameliorate age-associated cognitive decline by inducing training-specific neural plastic changes at both neural and behavioral levels. This longitudinal study examined the behavioral effects of a systematic thirteen-week cognitive training program on attention and working memory of older adults who were at risk of cognitive decline. These older adults were randomly assigned to the Cognitive Training Group (n=109 and the Active Control Group (n=100. Findings clearly indicated that training induced improvement in auditory and visual-spatial attention and working memory. The training effect was specific to the experience provided because no significant difference in verbal and visual-spatial memory between the two groups was observed. This pattern of findings is consistent with the prediction and the principle of experience-dependent neuroplasticity. Findings of our study provided further support to the notion that the neural plastic potential continues until older age. The baseline cognitive status did not correlate with pre- versus posttraining changes to any cognitive variables studied, suggesting that the initial cognitive status may not limit the neuroplastic potential of the brain at an old age.

  16. Neural Plastic Effects of Cognitive Training on Aging Brain.

    Science.gov (United States)

    Leung, Natalie T Y; Tam, Helena M K; Chu, Leung W; Kwok, Timothy C Y; Chan, Felix; Lam, Linda C W; Woo, Jean; Lee, Tatia M C

    2015-01-01

    Increasing research has evidenced that our brain retains a capacity to change in response to experience until late adulthood. This implies that cognitive training can possibly ameliorate age-associated cognitive decline by inducing training-specific neural plastic changes at both neural and behavioral levels. This longitudinal study examined the behavioral effects of a systematic thirteen-week cognitive training program on attention and working memory of older adults who were at risk of cognitive decline. These older adults were randomly assigned to the Cognitive Training Group (n = 109) and the Active Control Group (n = 100). Findings clearly indicated that training induced improvement in auditory and visual-spatial attention and working memory. The training effect was specific to the experience provided because no significant difference in verbal and visual-spatial memory between the two groups was observed. This pattern of findings is consistent with the prediction and the principle of experience-dependent neuroplasticity. Findings of our study provided further support to the notion that the neural plastic potential continues until older age. The baseline cognitive status did not correlate with pre- versus posttraining changes to any cognitive variables studied, suggesting that the initial cognitive status may not limit the neuroplastic potential of the brain at an old age.

  17. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills. Copyright © 2012 Elsevier B.V. All

  18. Neural activity during emotion recognition after combined cognitive plus social-cognitive training in schizophrenia

    Science.gov (United States)

    Hooker, Christine I.; Bruce, Lori; Fisher, Melissa; Verosky, Sara C.; Miyakawa, Asako; Vinogradov, Sophia

    2012-01-01

    Cognitive remediation training has been shown to improve both cognitive and social-cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social-cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 hour (10-week) remediation intervention which included both cognitive and social-cognitive training would influence neural function in regions that support social-cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 minutes/day] plus social-cognition training (SCT) which was focused on emotion recognition [~5–15 minutes per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. FMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social-cognition training impacts neural mechanisms that support social-cognition skills. PMID:22695257

  19. 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...

  20. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  1. 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.

  2. 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.

  3. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  4. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  5. Neural Mechanisms of Cognitive Dissonance (Revised): An EEG Study.

    Science.gov (United States)

    Colosio, Marco; Shestakova, Anna; Nikulin, Vadim V; Blagovechtchenski, Evgeny; Klucharev, Vasily

    2017-05-17

    Cognitive dissonance theory suggests that our preferences are modulated by the mere act of choosing. A choice between two similarly valued alternatives creates psychological tension (cognitive dissonance) that is reduced by a postdecisional reevaluation of the alternatives. We measured EEG of human subjects during rest and free-choice paradigm. Our study demonstrates that choices associated with stronger cognitive dissonance trigger a larger negative frontocentral evoked response similar to error-related negativity, which has in turn been implicated in general performance monitoring. Furthermore, the amplitude of the evoked response is correlated with the reevaluation of the alternatives. We also found a link between individual neural dynamics (long-range temporal correlations) of the frontocentral cortices during rest and follow-up neural and behavioral effects of cognitive dissonance. Individuals with stronger resting-state long-range temporal correlations demonstrated a greater postdecisional reevaluation of the alternatives and larger evoked brain responses associated with stronger cognitive dissonance. Thus, our results suggest that cognitive dissonance is reflected in both resting-state and choice-related activity of the prefrontal cortex as part of the general performance-monitoring circuitry. SIGNIFICANCE STATEMENT Contrary to traditional decision theory, behavioral studies repeatedly demonstrate that our preferences are modulated by the mere act of choosing. Difficult choices generate psychological (cognitive) dissonance, which is reduced by the postdecisional devaluation of unchosen options. We found that decisions associated with a higher level of cognitive dissonance elicited a stronger negative frontocentral deflection that peaked ∼60 ms after the response. This activity shares similar spatial and temporal features as error-related negativity, the electrophysiological correlate of performance monitoring. Furthermore, the frontocentral resting

  6. Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures

    Directory of Open Access Journals (Sweden)

    Yulia eSandamirskaya

    2014-01-01

    Full Text Available Dynamic Field Theory (DFT is an established framework for modelling embodied cognition. In DFT, elementary cognitive functions such as memory formation, formation of grounded representations, attentional processes, decision making, adaptation, and learning emerge from neuronal dynamics. The basic computational element of this framework is a Dynamic Neural Field (DNF. Under constraints on the time-scale of the dynamics, the DNF is computationally equivalent to a soft winner-take-all (WTA network, which is considered one of the basic computational units in neuronal processing. Recently, it has been shown how a WTA network may be implemented in neuromorphic hardware, such as analogue Very Large Scale Integration (VLSI device. This paper leverages the relationship between DFT and soft WTA networks to systematically revise and integrate established DFT mechanisms that have previously been spread among different architectures. In addition, I also identify some novel computational and architectural mechanisms of DFT which may be implemented in neuromorphic VLSI devices using WTA networks as an intermediate computational layer. These specific mechanisms include the stabilization of working memory, the coupling of sensory systems to motor dynamics, intentionality, and autonomous learning. I further demonstrate how all these elements may be integrated into a unified architecture to generate behavior and autonomous learning.

  7. Environmental effects on fish neural plasticity and cognition.

    Science.gov (United States)

    Ebbesson, L O E; Braithwaite, V A

    2012-12-01

    Most fishes experiencing challenging environments are able to adjust and adapt their physiology and behaviour to help them cope more effectively. Much of this flexibility is supported and influenced by cognition and neural plasticity. The understanding of fish cognition and the role played by different regions of the brain has improved significantly in recent years. Techniques such as lesioning, tract tracing and quantifying changes in gene expression help in mapping specialized brain areas. It is now recognized that the fish brain remains plastic throughout a fish's life and that it continues to be sensitive to environmental challenges. The early development of fish brains is shaped by experiences with the environment and this can promote positive and negative effects on both neural plasticity and cognitive ability. This review focuses on what is known about the interactions between the environment, the telencephalon and cognition. Examples are used from a diverse array of fish species, but there could be a lot to be gained by focusing research on neural plasticity and cognition in fishes for which there is already a wealth of knowledge relating to their physiology, behaviour and natural history, e.g. the Salmonidae. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

  8. 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.

  9. Neural activation associated with the cognitive emotion regulation of sadness in healthy children

    Directory of Open Access Journals (Sweden)

    Andy C. Belden

    2014-07-01

    Full Text Available When used effectively, cognitive reappraisal of distressing events is a highly adaptive cognitive emotion regulation (CER strategy, with impairments in cognitive reappraisal associated with greater risk for psychopathology. Despite extensive literature examining the neural correlates of cognitive reappraisal in healthy and psychiatrically ill adults, there is a dearth of data to inform the neural bases of CER in children, a key gap in the literature necessary to map the developmental trajectory of cognitive reappraisal. In this fMRI study, psychiatrically healthy schoolchildren were instructed to use cognitive reappraisal to modulate their emotional reactions and responses of negative affect after viewing sad photos. Consistent with the adult literature, when actively engaged in reappraisal compared to passively viewing sad photos, children showed increased activation in the vlPFC, dlPFC, and dmPFC as well as in parietal and temporal lobe regions. When children used cognitive reappraisal to minimize their experience of negative affect after viewing sad stimuli they exhibited dampened amygdala responses. Results are discussed in relation to the importance of identifying and characterizing neural processes underlying adaptive CER strategies in typically developing children in order to understand how these systems go awry and relate to the risk and occurrence of affective disorders.

  10. Media Multitasking and Cognitive, Psychological, Neural, and Learning Differences.

    Science.gov (United States)

    Uncapher, Melina R; Lin, Lin; Rosen, Larry D; Kirkorian, Heather L; Baron, Naomi S; Bailey, Kira; Cantor, Joanne; Strayer, David L; Parsons, Thomas D; Wagner, Anthony D

    2017-11-01

    American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). This phenomenon is not limited to American youth but is paralleled across the globe. Given that a large number of media multitaskers (MMTs) are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs. It is critical to understand the relation between the relevant cognitive domains and underlying neural structure and function. Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments. The present review surveys the growing body of evidence demonstrating that heavy MMTs show differences in cognition (eg, poorer memory), psychosocial behavior (eg, increased impulsivity), and neural structure (eg, reduced volume in anterior cingulate cortex). Furthermore, research indicates that multitasking with media during learning (in class or at home) can negatively affect academic outcomes. Until the direction of causality is understood (whether media multitasking causes such behavioral and neural differences or whether individuals with such differences tend to multitask with media more often), the data suggest that engagement with concurrent media streams should be thoughtfully considered. Findings from such research promise to inform policy and practice on an increasingly urgent societal issue while significantly advancing our understanding of the intersections between cognitive, psychosocial, neural, and academic factors. Copyright © 2017 by the American Academy of Pediatrics.

  11. Behavioral, Cognitive and Neural Markers of Asperger Syndrome

    Directory of Open Access Journals (Sweden)

    Farnaz Faridi

    2017-09-01

    Full Text Available Asperger syndrome (AS is a subtype of Autism Spectrum Disorder (ASD characterized by major problems in social and nonverbal communication, together with limited and repetitive forms of behavior and interests. The linguistic and cognitive development in AS is preserved which help us to differentiate it from other subtypes of ASD. However, significant effects of AS on cognitive abilities and brain functions still need to be researched. Although a clear cut pathology for Asperger has not been identified yet, recent studies have largely focused on brain imaging techniques to investigate AS. In this regard, we carried out a systematic review on behavioral, cognitive, and neural markers (specifically using MRI and fMRI studies on AS. In this paper, behavior, motor skills and language capabilities of individuals with Asperger are compared to those in healthy controls. In addition, common findings across MRI and fMRI based studies associated with behavior and cognitive disabilities are highlighted. 

  12. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  13. Cognitive and Neural Correlates of Aging in Autism Spectrum Disorder

    Science.gov (United States)

    2017-07-01

    this study. B. Blair Braden, PhD has been active in all of our projects on ASD and aging . She came to the lab after graduate training in...i. Aging in Autism Special Interest Group, International Society for Autism Research (INSAR). Both Drs. Baxter and Braden actively participated...AWARD NUMBER: W81XWH-15-1-0211 TITLE: Cognitive and Neural Correlates of Aging in Autism Spectrum Disorder PRINCIPAL INVESTIGATOR: Leslie C

  14. A Neural Information Field Approach to Computational Cognition

    Science.gov (United States)

    2016-11-18

    effects of distraction during list memory . These distractions include short and long delays before recall, and continuous distraction (forced rehearsal... memory encoding and replay in hippocampus. Computational Neuroscience Society (CNS), p. 166, 2014. D. A. Pinotsis, Neural Field Coding of Short Term ...performance of children learning to count in a SPA model; proposed a new SPA model of cognitive load using the N-back task; developed a new model of the

  15. Neural systems for social cognition: gray matter volume abnormalities in boys at high genetic risk of autism symptoms, and a comparison with idiopathic autism spectrum disorder.

    Science.gov (United States)

    Goddard, Marcia N; Swaab, Hanna; Rombouts, Serge A R B; van Rijn, Sophie

    2016-09-01

    Klinefelter syndrome (47, XXY) is associated with several physical, cognitive, and behavioral consequences. In terms of social development, there is an increased risk of autism symptomatology. However, it remains unclear how social deficits are related to abnormal brain development and to what degree underlying mechanisms of social dysfunction in 47, XXY are similar to, or different from, those in idiopathic autism (ASD). This study was aimed at investigating the neural architecture of brain structures related to social information processing in boys with 47, XXY, also in comparison with boys with idiopathic ASD. MRI scans of 16 boys with 47, XXY, 16 with ASD, and 16 nonclinical, male controls were analyzed using voxel-based morphometry (VBM). A region of interest mask containing the superior temporal cortex, amygdala, orbitofrontal cortex (OFC), insular cortex, and medial frontal cortex was used. The Social Responsiveness Scale (SRS) was used to assess degree of autism spectrum symptoms. The 47, XXY group could not be distinguished from the ASD group on mean SRS scores, and their scores were significantly higher than in controls. VBM showed that boys with 47, XXY have significant gray matter volume reductions in the left and right insula, and the left OFC, compared with controls and boys with ASD. Additionally, boys with 47, XXY had significantly less gray matter in the right superior temporal gyrus than controls. These results imply social challenges associated with 47, XXY may be rooted in neural anatomy, and autism symptoms in boys with 47, XXY and boys with ASD might have, at least partially, different underlying etiologies.

  16. Expert music performance: cognitive, neural, and developmental bases.

    Science.gov (United States)

    Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

    2015-01-01

    In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

  17. Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

    International Nuclear Information System (INIS)

    Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun

    2005-01-01

    It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 ± 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning

  18. Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

    2005-07-01

    It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 {+-} 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning.

  19. Neural circuitry of emotional and cognitive conflict revealed through facial expressions.

    Science.gov (United States)

    Chiew, Kimberly S; Braver, Todd S

    2011-03-09

    Neural systems underlying conflict processing have been well studied in the cognitive realm, but the extent to which these overlap with those underlying emotional conflict processing remains unclear. A novel adaptation of the AX Continuous Performance Task (AX-CPT), a stimulus-response incompatibility paradigm, was examined that permits close comparison of emotional and cognitive conflict conditions, through the use of affectively-valenced facial expressions as the response modality. Brain activity was monitored with functional magnetic resonance imaging (fMRI) during performance of the emotional AX-CPT. Emotional conflict was manipulated on a trial-by-trial basis, by requiring contextually pre-cued facial expressions to emotional probe stimuli (IAPS images) that were either affectively compatible (low-conflict) or incompatible (high-conflict). The emotion condition was contrasted against a matched cognitive condition that was identical in all respects, except that probe stimuli were emotionally neutral. Components of the brain cognitive control network, including dorsal anterior cingulate cortex (ACC) and lateral prefrontal cortex (PFC), showed conflict-related activation increases in both conditions, but with higher activity during emotion conditions. In contrast, emotion conflict effects were not found in regions associated with affective processing, such as rostral ACC. These activation patterns provide evidence for a domain-general neural system that is active for both emotional and cognitive conflict processing. In line with previous behavioural evidence, greatest activity in these brain regions occurred when both emotional and cognitive influences additively combined to produce increased interference.

  20. Banknote recognition: investigating processing and cognition framework using competitive neural network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-02-01

    Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

  1. 1st International Conference on Cognitive Systems and Information Processing

    CERN Document Server

    Hu, Dewen; Liu, Huaping

    2014-01-01

    "Foundations and Practical Applications of Cognitive Systems and Information Processing" presents selected papers from the First International Conference on Cognitive Systems and Information Processing, held in Beijing, China on December 15-17, 2012 (CSIP2012). The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in artificial cognitive systems and advanced information processing, and to present new findings and perspectives on future development. This book introduces multidisciplinary perspectives on the subject areas of Cognitive Systems and Information Processing, including cognitive sciences and technology, autonomous vehicles, cognitive psychology, cognitive metrics, information fusion, image/video understanding, brain-computer interfaces, visual cognitive processing, neural computation, bioinformatics, etc. The book will be beneficial for both researchers and practitioners in the fields of Cognitive Science, Computer Science and Cogni...

  2. Cognitive and Neural Sciences Division 1991 Programs

    Science.gov (United States)

    1991-08-01

    interventions , for performance aiding, for certification and for performance evaluation. As the Navy modernizes those systems to take advantage of potential...2223-2237. Livingstone, M., Drislane, F. and Galaburda, A. (1991, in press) Physiological evidence for a magnocellular defect in dyslexia . Science. 201...develop training interventions to counter the effects of stress on performance. Progress: This grant is new in FY91. Outside Funding: Funds for this

  3. Neural dynamics of the cognitive map in the hippocampus.

    Science.gov (United States)

    Wagatsuma, Hiroaki; Yamaguchi, Yoko

    2007-06-01

    The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may

  4. Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

    Science.gov (United States)

    Gold, Brian T; Kim, Chobok; Johnson, Nathan F; Kryscio, Richard J; Smith, Charles D

    2013-01-09

    Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task-switching paradigm, including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task-switching experiment while functional magnetic resonance imaging (fMRI) was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task-switching performance. In addition, the lower blood oxygenation level-dependent response in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes.

  5. Age-related neural correlates of cognitive task performance under increased postural load

    NARCIS (Netherlands)

    Van Impe, A; Bruijn, S M; Coxon, J P; Wenderoth, N; Sunaert, S; Duysens, J; Swinnen, S P

    2013-01-01

    Behavioral studies suggest that postural control requires increased cognitive control and visuospatial processing with aging. Consequently, performance can decline when concurrently performing a postural and a demanding cognitive task. We aimed to identify the neural substrate underlying this

  6. False memories with age: neural and cognitive underpinnings

    Science.gov (United States)

    Devitt, Aleea L.; Schacter, Daniel L.

    2016-01-01

    As we age we become increasingly susceptible to memory distortions and inaccuracies. Over the past decade numerous neuroimaging studies have attempted to illuminate the neural underpinnings of aging and false memory. Here we review these studies, and link their findings with those concerning the cognitive properties of age-related changes in memory accuracy. Collectively this evidence points towards a prominent role for age-related declines in medial temporal and prefrontal brain areas, and corresponding impairments in associative binding and strategic monitoring. A resulting cascade of cognitive changes contributes to the heightened vulnerability to false memories with age, including reduced recollective ability, a reliance on gist information and familiarity-based monitoring mechanisms, as well as a reduced ability to inhibit irrelevant information and erroneous binding of features between memory traces. We consider both theoretical and applied implications of research on aging and false memories, as well as questions remaining to be addressed in future research. PMID:27592332

  7. Cognitive-Neural Effects of Brush Writing of Chinese Characters: Cortical Excitation of Theta Rhythm

    Directory of Open Access Journals (Sweden)

    Min Xu

    2013-01-01

    Full Text Available Chinese calligraphy has been scientifically investigated within the contexts and principles of psychology, cognitive science, and the cognitive neuroscience. On the basis of vast amount of research in the last 30 years, we have developed a cybernetic theory of handwriting and calligraphy to account for the intricate interactions of several psychological dimensions involved in the dynamic act of graphic production. Central to this system of writing are the role of sensory, bio-, cognitive, and neurofeedback mechanisms for the initiation, guidance, and regulation of the writing motions vis-a-vis visual-geometric variations of Chinese characters. This experiment provided the first evidence of cortical excitation in EEG theta wave as a neural hub that integrates information coming from changes in the practitioner’s body, emotions, and cognition. In addition, it has also confirmed neurofeedback as an essential component of the cybernetic theory of handwriting and calligraphy.

  8. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    Science.gov (United States)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  9. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  10. The dissociable neural dynamics of cognitive conflict and emotional conflict control: An ERP study.

    Science.gov (United States)

    Xue, Song; Li, Yu; Kong, Xia; He, Qiaolin; Liu, Jia; Qiu, Jiang

    2016-04-21

    This study investigated differences in the neural time-course of cognitive conflict and emotional conflict control, using event-related potentials (ERPs). Although imaging studies have provided some evidence that distinct, dissociable neural systems underlie emotional and nonemotional conflict resolution, no ERP study has directly compared these two types of conflict. Therefore, the present study used a modified face-word Stroop task to explore the electrophysiological correlates of cognitive and emotional conflict control. The behavioral data showed that the difference in response time of congruency (incongruent condition minus the congruent condition) was larger in the cognitive conflict task than in the emotional conflict task, which indicated that cognitive conflict was stronger than the emotional conflict in the present tasks. Analysis of the ERP data revealed a main effect of task type on N2, which may be associated with top-down attention. The N450 results showed an interaction between cognitive and emotional conflict, which might be related to conflict detection. In addition, we found the incongruent condition elicited a larger SP than the congruent condition, which might be related to conflict resolution. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  11. Selective attention on representations in working memory: cognitive and neural mechanisms.

    Science.gov (United States)

    Ku, Yixuan

    2018-01-01

    Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

  12. Selective attention on representations in working memory: cognitive and neural mechanisms

    Directory of Open Access Journals (Sweden)

    Yixuan Ku

    2018-04-01

    Full Text Available Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

  13. Distinct neural correlates of emotional and cognitive empathy in older adults.

    Science.gov (United States)

    Moore, Raeanne C; Dev, Sheena I; Jeste, Dilip V; Dziobek, Isabel; Eyler, Lisa T

    2015-04-30

    Empathy is thought to be a mechanism underlying prosocial behavior across the lifespan, yet little is known about how levels of empathy relate to individual differences in brain functioning among older adults. In this exploratory study, we examined the neural correlates of affective and cognitive empathy in older adults. Thirty older adults (M=79 years) underwent fMRI scanning and neuropsychological testing and completed a test of affective and cognitive empathy. Brain response during processing of cognitive and emotional stimuli was measured by fMRI in a priori and task-related regions and was correlated with levels of empathy. Older adults with higher levels of affective empathy showed more deactivation in the amygdala and insula during a working memory task, whereas those with higher cognitive empathy showed greater insula activation during a response inhibition task. Our preliminary findings suggest that brain systems linked to emotional and social processing respond differently among older adults with more or less affective and cognitive empathy. That these relationships can be seen both during affective and non-emotional tasks of "cold" cognitive abilities suggests that empathy may impact social behavior through both emotional and cognitive mechanisms. Published by Elsevier Ireland Ltd.

  14. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  15. Culture-sensitive neural substrates of human cognition: a transcultural neuroimaging approach.

    Science.gov (United States)

    Han, Shihui; Northoff, Georg

    2008-08-01

    Our brains and minds are shaped by our experiences, which mainly occur in the context of the culture in which we develop and live. Although psychologists have provided abundant evidence for diversity of human cognition and behaviour across cultures, the question of whether the neural correlates of human cognition are also culture-dependent is often not considered by neuroscientists. However, recent transcultural neuroimaging studies have demonstrated that one's cultural background can influence the neural activity that underlies both high- and low-level cognitive functions. The findings provide a novel approach by which to distinguish culture-sensitive from culture-invariant neural mechanisms of human cognition.

  16. Neuromorphic cognitive systems a learning and memory centered approach

    CERN Document Server

    Yu, Qiang; Hu, Jun; Tan Chen, Kay

    2017-01-01

    This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromo...

  17. 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…

  18. False memories with age: Neural and cognitive underpinnings.

    Science.gov (United States)

    Devitt, Aleea L; Schacter, Daniel L

    2016-10-01

    As we age we become increasingly susceptible to memory distortions and inaccuracies. Over the past decade numerous neuroimaging studies have attempted to illuminate the neural underpinnings of aging and false memory. Here we review these studies, and link their findings with those concerning the cognitive properties of age-related changes in memory accuracy. Collectively this evidence points towards a prominent role for age-related declines in medial temporal and prefrontal brain areas, and corresponding impairments in associative binding and strategic monitoring. A resulting cascade of cognitive changes contributes to the heightened vulnerability to false memories with age, including reduced recollective ability, a reliance on gist information and familiarity-based monitoring mechanisms, as well as a reduced ability to inhibit irrelevant information and erroneous binding of features between memory traces. We consider both theoretical and applied implications of research on aging and false memories, as well as questions remaining to be addressed in future research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Neurally-dissociable cognitive components of reading deficits in subacute stroke

    Directory of Open Access Journals (Sweden)

    Olga eBoukrina

    2015-05-01

    Full Text Available According to cognitive models of reading, words are processed by interacting orthographic (spelling, phonological (sound and semantic (meaning information. Despite extensive study of the neural basis of reading in healthy participants, little group data exist on patients with reading deficits from focal brain damage pointing to critical neural systems for reading. Here we report on one such study. We have performed neuropsychological testing and MRI on 11 patients with left-hemisphere stroke (<= 5 weeks post stroke. Patients completed tasks assessing cognitive components of reading such as semantics (matching picture or word choices to a target based on meaning, phonology (matching word choices to a target based on rhyming, and orthography (a two-alternative forced choice of the most plausible nonword. They also read aloud pseudowords and words with high or low levels of usage frequency, imageability, and spelling-sound consistency. As predicted by the cognitive model, when averaged across patients, the influence of semantics was most salient for low-frequency, low-consistency words, when phonological decoding is especially difficult. Qualitative subtraction analyses revealed lesion sites specific to phonological processing. These areas were consistent with those shown previously to activate for phonology in healthy participants, including supramarginal, posterior superior temporal, middle temporal, inferior frontal gyri, and underlying white matter. Notable divergence between this analysis and previous functional imaging is the association of lesions in the mid-fusiform gyrus and anterior temporal lobe with phonological reading deficits. This study represents progress toward identifying brain lesion-deficit relationships in the cognitive components of reading. Such correspondences are expected to help not only better understand the neural mechanisms of reading, but may also help tailor reading therapy to individual neurocognitive deficit

  20. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

  1. Neural systems for social cognition : gray matter volume abnormalities in boys at high genetic risk of autism symptoms, and a comparison with idiopathic autism spectrum disorder.

    NARCIS (Netherlands)

    Goddard, M.N.; Swaab, H.; Rombouts, S.A.; Van, Rijn S.

    2016-01-01

    Klinefelter syndrome (47, XXY) is associated with several physical, cognitive, and behavioral consequences. In terms of social development, there is an increased risk of autism symptomatology. However, it remains unclear how social deficits are related to abnormal brain development and to what

  2. 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.

  3. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  4. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  5. Neural implementation of operations used in quantum cognition.

    Science.gov (United States)

    Busemeyer, Jerome R; Fakhari, Pegah; Kvam, Peter

    2017-11-01

    Quantum probability theory has been successfully applied outside of physics to account for numerous findings from psychology regarding human judgement and decision making behavior. However, the researchers who have made these applications do not rely on the hypothesis that the brain is some type of quantum computer. This raises the question of how could the brain implement quantum algorithms other than quantum physical operations. This article outlines one way that a neural based system could perform the computations required by applications of quantum probability to human behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. 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.

  7. Anticipatory Cognitive Systems: a Theoretical Model

    Science.gov (United States)

    Terenzi, Graziano

    This paper deals with the problem of understanding anticipation in biological and cognitive systems. It is argued that a physical theory can be considered as biologically plausible only if it incorporates the ability to describe systems which exhibit anticipatory behaviors. The paper introduces a cognitive level description of anticipation and provides a simple theoretical characterization of anticipatory systems on this level. Specifically, a simple model of a formal anticipatory neuron and a model (i.e. the τ-mirror architecture) of an anticipatory neural network which is based on the former are introduced and discussed. The basic feature of this architecture is that a part of the network learns to represent the behavior of the other part over time, thus constructing an implicit model of its own functioning. As a consequence, the network is capable of self-representation; anticipation, on a oscopic level, is nothing but a consequence of anticipation on a microscopic level. Some learning algorithms are also discussed together with related experimental tasks and possible integrations. The outcome of the paper is a formal characterization of anticipation in cognitive systems which aims at being incorporated in a comprehensive and more general physical theory.

  8. Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors.

    Directory of Open Access Journals (Sweden)

    Jessica R Andrews-Hanna

    Full Text Available Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established.To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

  9. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  10. Cooperative and cognitive satellite systems

    CERN Document Server

    Chatzinotas, Symeon; De Gaudenzi, Riccardo

    2015-01-01

    Cooperative and Cognitive Satellite Systems provides a solid overview of the current research in the field of cooperative and cognitive satellite systems, helping users understand how to incorporate state-of-the-art communication techniques in innovative satellite network architectures to enable the next generation of satellite systems. The book is edited and written by top researchers and practitioners in the field, providing a comprehensive explanation of current research that allows users to discover future technologies and their applications, integrate satellite and terrestrial systems

  11. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum.

    Science.gov (United States)

    Dickinson, Abigail; DiStefano, Charlotte; Senturk, Damla; Jeste, Shafali Spurling

    2018-03-01

    Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  12. The Application of Cognitive Diagnostic Approaches via Neural Network Analysis of Serious Educational Games

    Science.gov (United States)

    Lamb, Richard L.

    Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student

  13. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

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

  14. Cognitive Medical Multiagent Systems

    OpenAIRE

    Barna Iantovics

    2010-01-01

    The development of efficient and flexible agent-based medical diagnosis systems represents a recent research direction. Medical multiagent systems may improve the efficiency of traditionally developed medical computational systems, like the medical expert systems. In our previous researches, a novel cooperative medical diagnosis multiagent system called CMDS (Contract Net Based Medical Diagnosis System) was proposed. CMDS system can solve flexibly a large variety of medical diagnosis problems...

  15. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  16. Cognitive Medical Multiagent Systems

    Directory of Open Access Journals (Sweden)

    Barna Iantovics

    2010-01-01

    Full Text Available The development of efficient and flexible agent-based medical diagnosis systems represents a recent research direction. Medical multiagent systems may improve the efficiency of traditionally developed medical computational systems, like the medical expert systems. In our previous researches, a novel cooperative medical diagnosis multiagent system called CMDS (Contract Net Based Medical Diagnosis System was proposed. CMDS system can solve flexibly a large variety of medical diagnosis problems. This paper analyses the increased intelligence of the CMDS system, which motivates its use for different medical problem’s solving.

  17. Advances in Cognitive Information Systems

    CERN Document Server

    Ogiela, Lidia

    2012-01-01

    The development of computer science is now so rapid that we, the readers, in-creasingly receive technology news about new solutions and applications which very often straddle the border between the real and the virtual worlds. Computer science is also the area in which cognitive science is witnessing a renaissance, be-cause its combination with technical sciences has given birth to a broad scientific discipline called cognitive informatics. And it is this discipline which has become the main theme of this monograph, which is also to serve as a kind of guide to cognitive informatics problems. This book is the result of work on systems for the cognitive analysis and inter-pretation of various data. The purpose of such an analytical approach is to show that for an in-depth analysis of data, the layers of semantics contained in these sets must be taken into account. The interdisciplinary nature of the solutions proposed means that the subject of cognitive systems forming part of cognitive informatics becomes a ne...

  18. A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology

    Science.gov (United States)

    Bundesen, Claus; Habekost, Thomas; Kyllingsbaek, Soren

    2005-01-01

    A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing resources (cells) are devoted to behaviorally…

  19. Moderating Effects of Cortisol on Neural-Cognitive Association in Cognitively Normal Elderly Subjects

    Directory of Open Access Journals (Sweden)

    Way K. W. Lau

    2017-05-01

    Full Text Available Cortisol homeostasis is important for healthy brain and cognitive aging. The aim of the current study is to investigate the role of serum cortisol levels in the relationship between regional brain volumes and cognitive processing speed in a group of cognitively normal elderly subjects. Forty-one healthy elderly participants were from a parallel longitudinal study. The reported data in this study reflects baseline measurements. Whole-brain anatomical scanning was performed using a 3.0 Tesla Philips Medical Systems Achieva scanner. Cognitive processing speed was assessed by the digit-symbol and symbol search tests, from the Chinese version of the Wechsler Adult Intelligence Scale—third edition (WAIS-III. Serum cortisol levels (sampled in the late morning were measured by ELISA kits. Whole-brain regression analysis revealed that serum cortisol levels positively predicted the white matter volumes (WMV of the right thalamus, the gray matter volumes (GMV of the left thalamus and right cerebellar tonsil, and negatively predicted the WMV and GMV of the left middle temporal gyrus (MTG in 41 healthy elderly participants. Furthermore, serum cortisol significantly moderated the relationship between the GMV of the left MTG and processing speed, as well as the GMV of the left thalamus and processing speed. This study provided the first piece of evidence supporting serum cortisol levels in moderating the relationship between regional brain volumes and processing speed in healthy elderly subjects. This observation enriches our understanding of the role of cortisol in brain morphology and cognitive functioning.

  20. Moderating Effects of Cortisol on Neural-Cognitive Association in Cognitively Normal Elderly Subjects

    Science.gov (United States)

    Lau, Way K. W.; Leung, Mei Kei; Law, Andrew C. K.; Lee, Tatia M. C.

    2017-01-01

    Cortisol homeostasis is important for healthy brain and cognitive aging. The aim of the current study is to investigate the role of serum cortisol levels in the relationship between regional brain volumes and cognitive processing speed in a group of cognitively normal elderly subjects. Forty-one healthy elderly participants were from a parallel longitudinal study. The reported data in this study reflects baseline measurements. Whole-brain anatomical scanning was performed using a 3.0 Tesla Philips Medical Systems Achieva scanner. Cognitive processing speed was assessed by the digit-symbol and symbol search tests, from the Chinese version of the Wechsler Adult Intelligence Scale—third edition (WAIS-III). Serum cortisol levels (sampled in the late morning) were measured by ELISA kits. Whole-brain regression analysis revealed that serum cortisol levels positively predicted the white matter volumes (WMV) of the right thalamus, the gray matter volumes (GMV) of the left thalamus and right cerebellar tonsil, and negatively predicted the WMV and GMV of the left middle temporal gyrus (MTG) in 41 healthy elderly participants. Furthermore, serum cortisol significantly moderated the relationship between the GMV of the left MTG and processing speed, as well as the GMV of the left thalamus and processing speed. This study provided the first piece of evidence supporting serum cortisol levels in moderating the relationship between regional brain volumes and processing speed in healthy elderly subjects. This observation enriches our understanding of the role of cortisol in brain morphology and cognitive functioning. PMID:28596732

  1. Differential effects of erythropoietin on neural and cognitive measures of executive function 3 and 7 days post-administration

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla; Inkster, Becky; O'Sullivan, Ursula

    2008-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects and improves cognitive function in animal models of neurodegenerative and neuropsychiatric illness. In humans, weekly Epo administration over 3 months improves cognitive function in schizophrenia. The neural underpinnings and time...

  2. Neural correlates of reappraisal considering working memory capacity and cognitive flexibility.

    Science.gov (United States)

    Zaehringer, Jenny; Falquez, Rosalux; Schubert, Anna-Lena; Nees, Frauke; Barnow, Sven

    2018-01-09

    Cognitive reappraisal of emotion is strongly related to long-term mental health. Therefore, the exploration of underlying cognitive and neural mechanisms has become an essential focus of research. Considering that reappraisal and executive functions rely on a similar brain network, the question arises whether behavioral differences in executive functions modulate neural activity during reappraisal. Using functional neuroimaging, the present study aimed to analyze the role of working memory capacity (WMC) and cognitive flexibility in brain activity during down-regulation of negative emotions by reappraisal in N = 20 healthy participants. Results suggests that WMC and cognitive flexibility were negatively correlated with prefrontal activity during reappraisal condition. Here, results also revealed a negative correlation between cognitive flexibility and amygdala activation. These findings provide first hints that (1) individuals with lower WMC and lower cognitive flexibility might need more higher-order cognitive neural resources in order to down-regulate negative emotions and (2) cognitive flexibility relates to emotional reactivity during reappraisal.

  3. Spiking neural P systems with multiple channels.

    Science.gov (United States)

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

    2017-11-01

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

  4. Theta phase precession and phase selectivity: a cognitive device description of neural coding

    Science.gov (United States)

    Zalay, Osbert C.; Bardakjian, Berj L.

    2009-06-01

    Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to

  5. Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.

    Science.gov (United States)

    Lusch, Bethany; Weholt, Jake; Maia, Pedro D; Kutz, J Nathan

    2018-06-01

    The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning.

    Science.gov (United States)

    Daniel, Reka; Pollmann, Stefan

    2010-01-06

    The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

  7. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

  8. Mobile Phones as Cognitive Systems

    DEFF Research Database (Denmark)

    Stopczynski, Arkadiusz

    the importance of the proposed approach and deployed implementation. The second part of the thesis deals with expanding our capabilities to sense the cognitive and emotional state of the users through development of a system for mobile brain imaging—the Smartphone Brain Scanner. A developed framework allows...

  9. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  10. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    Science.gov (United States)

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  11. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  12. Systematic review of the neural basis of social cognition in patients with mood disorders.

    Science.gov (United States)

    Cusi, Andrée M; Nazarov, Anthony; Holshausen, Katherine; Macqueen, Glenda M; McKinnon, Margaret C

    2012-05-01

    This review integrates neuroimaging studies of 2 domains of social cognition--emotion comprehension and theory of mind (ToM)--in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were "fMRI," "emotion comprehension," "emotion perception," "affect comprehension," "affect perception," "facial expression," "prosody," "theory of mind," "mentalizing" and "empathy" in combination with "major depressive disorder," "bipolar disorder," "major depression," "unipolar depression," "clinical depression" and "mania." Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. Studies that did not include control tasks or a comparator group were included in this review. Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks under lying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders.

  13. Concentration: The Neural Underpinnings of How Cognitive Load Shields Against Distraction.

    Science.gov (United States)

    Sörqvist, Patrik; Dahlström, Örjan; Karlsson, Thomas; Rönnberg, Jerker

    2016-01-01

    Whether cognitive load-and other aspects of task difficulty-increases or decreases distractibility is subject of much debate in contemporary psychology. One camp argues that cognitive load usurps executive resources, which otherwise could be used for attentional control, and therefore cognitive load increases distraction. The other camp argues that cognitive load demands high levels of concentration (focal-task engagement), which suppresses peripheral processing and therefore decreases distraction. In this article, we employed an functional magnetic resonance imaging (fMRI) protocol to explore whether higher cognitive load in a visually-presented task suppresses task-irrelevant auditory processing in cortical and subcortical areas. The results show that selectively attending to an auditory stimulus facilitates its neural processing in the auditory cortex, and switching the locus-of-attention to the visual modality decreases the neural response in the auditory cortex. When the cognitive load of the task presented in the visual modality increases, the neural response to the auditory stimulus is further suppressed, along with increased activity in networks related to effortful attention. Taken together, the results suggest that higher cognitive load decreases peripheral processing of task-irrelevant information-which decreases distractibility-as a side effect of the increased activity in a focused-attention network.

  14. 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

  15. Neural information processing in cognition: we start to understand the orchestra, but where is the conductor?

    Directory of Open Access Journals (Sweden)

    Guenther ePalm

    2016-01-01

    Full Text Available Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.

  16. Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?

    Science.gov (United States)

    Palm, Günther

    2016-01-01

    Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. PMID:26858632

  17. Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

    Directory of Open Access Journals (Sweden)

    Todor Petkov

    2013-12-01

    Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.

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

    Science.gov (United States)

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

    2012-01-01

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

  19. Social cognition and neural substrates of face perception: implications for neurodevelopmental and neuropsychiatric disorders.

    Science.gov (United States)

    Lazar, Steven M; Evans, David W; Myers, Scott M; Moreno-De Luca, Andres; Moore, Gregory J

    2014-04-15

    Social cognition is an important aspect of social behavior in humans. Social cognitive deficits are associated with neurodevelopmental and neuropsychiatric disorders. In this study we examine the neural substrates of social cognition and face processing in a group of healthy young adults to examine the neural substrates of social cognition. Fifty-seven undergraduates completed a battery of social cognition tasks and were assessed with electroencephalography (EEG) during a face-perception task. A subset (N=22) were administered a face-perception task during functional magnetic resonance imaging. Variance in the N170 EEG was predicted by social attribution performance and by a quantitative measure of empathy. Neurally, face processing was more bilateral in females than in males. Variance in fMRI voxel count in the face-sensitive fusiform gyrus was predicted by quantitative measures of social behavior, including the Social Responsiveness Scale (SRS) and the Empathizing Quotient. When measured as a quantitative trait, social behaviors in typical and pathological populations share common neural pathways. The results highlight the importance of viewing neurodevelopmental and neuropsychiatric disorders as spectrum phenomena that may be informed by studies of the normal distribution of relevant traits in the general population. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Imaging the neural effects of cognitive bias modification training

    NARCIS (Netherlands)

    Wiers, C.E.; Wiers, R.W.

    Cognitive bias modification (CBM) was first developed as an experimental tool to examine the causal role of cognitive biases, and later developed into complementary interventions in experimental psychopathology research. CBM involves the "re-training" of implicit biases by means of multiple trials

  1. Age-Related Cognitive Impairments in Mice with a Conditional Ablation of the Neural Cell Adhesion Molecule

    Science.gov (United States)

    Bisaz, Reto; Boadas-Vaello, Pere; Genoux, David; Sandi, Carmen

    2013-01-01

    Most of the mechanisms involved in neural plasticity support cognition, and aging has a considerable effect on some of these processes. The neural cell adhesion molecule (NCAM) of the immunoglobulin superfamily plays a pivotal role in structural and functional plasticity and is required to modulate cognitive and emotional behaviors. However,…

  2. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  3. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Directory of Open Access Journals (Sweden)

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  4. Investigating the Neural Correlates of Emotion–Cognition Interaction Using an Affective Stroop Task

    Directory of Open Access Journals (Sweden)

    Nora M. Raschle

    2017-09-01

    Full Text Available The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula. When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in

  5. Cognitive Bias in Systems Verification

    Science.gov (United States)

    Larson, Steve

    2012-01-01

    Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

  6. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

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

  7. 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

  8. COMT val108/158 met genotype affects neural but not cognitive processing in healthy individuals.

    Science.gov (United States)

    Dennis, Nancy A; Need, Anna C; LaBar, Kevin S; Waters-Metenier, Sheena; Cirulli, Elizabeth T; Kragel, James; Goldstein, David B; Cabeza, Roberto

    2010-03-01

    The relationship between cognition and a functional polymorphism in the catechol-O-methlytransferase (COMT) gene, val108/158met, is one of debate in the literature. Furthermore, based on the dopaminergic differences associated with the COMT val108/158met genotype, neural differences during cognition may be present, regardless of genotypic differences in cognitive performance. To investigate these issues the current study aimed to 1) examine the effects of COMT genotype using a large sample of healthy individuals (n = 496-1218) and multiple cognitive measures, and using a subset of the sample (n = 22), 2) examine whether COMT genotype effects medial temporal lobe (MTL) and frontal activity during successful relational memory processing, and 3) investigate group differences in functional connectivity associated with successful relational memory processing. Results revealed no significant group difference in cognitive performance between COMT genotypes in any of the 19 cognitive measures. However, in the subset sample, COMT val homozygotes exhibited significantly decreased MTL and increased prefrontal activity during both successful relational encoding and retrieval, and reduced connectivity between these regions compared with met homozygotes. Taken together, the results suggest that although the COMT val108/158met genotype has no effect on cognitive behavioral measures in healthy individuals, it is associated with differences in neural process underlying cognitive output.

  9. Towards a unifying neural theory of social cognition

    NARCIS (Netherlands)

    Keysers, Christian; Gazzola, Valeria; Anders, S; Ende, G; Junghoffer, M; Kissler, J; Wildgruber, D

    2006-01-01

    Humans can effortlessly understand a lot of what is going on in other peoples' minds. Understanding the neural basis of this capacity has proven quite difficult. Since the discovery of mirror neurons, a number of successful experiments have approached the question of how we understand the actions of

  10. Neural Computation and the Computational Theory of Cognition

    Science.gov (United States)

    Piccinini, Gualtiero; Bahar, Sonya

    2013-01-01

    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism--neural processes are computations in the…

  11. Neural dynamics of the cognitive map in the hippocampus

    OpenAIRE

    Wagatsuma, Hiroaki; Yamaguchi, Yoko

    2007-01-01

    The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading t...

  12. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  13. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  14. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  15. Reward-based training of recurrent neural networks for cognitive and value-based tasks.

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-13

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

  16. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  17. An Examination of Application of Artificial Neural Network in Cognitive Radios

    International Nuclear Information System (INIS)

    Salau, H Bello; Onwuka, E N; Aibinu, A M

    2013-01-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

  18. Neural, cognitive, and neuroimaging markers of the suicidal brain

    Directory of Open Access Journals (Sweden)

    Sobanski T

    2015-05-01

    Full Text Available Thomas Sobanski,1 Karl-Jürgen Bär,2 Gerd Wagner2 1Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Thüringen-Kliniken "Georgius Agricola" GmbH, Saalfeld, Germany; 2Department of Psychiatry and Psychotherapy, Psychiatric Brain and Body Research Group Jena, Jena University Hospital, Jena, GermanyAbstract: Suicidal behavior (SB is characterized by the occurrence of suicide attempts with substantial intent to die. SB is a major health problem worldwide. In the great majority of cases, SB occurs in patients suffering from psychiatric disorders, mainly from affective disorders or schizophrenia. Despite this high association, there is growing evidence from genetic studies that SB might represent a psychiatric condition on its own. This review provides an overview of the most significant neurobiological and neurocognitive findings in SB. We provide evidence for specific dysfunctions within the serotonergic system, for distinct morphological abnormalities in the gray and white matter composition as well as for neurofunctional alterations in the fronto-striatal network. Additionally, the putative role of impulsivity and hopelessness as trait-like risk factors for SB is outlined. Both the personality traits are associated with altered prefrontal cortex function and deficits in cognitive and affective control similar to the findings in SB. Given the difficulties of clinical risk assessment, there is a need to identify specific markers that can predict SB more reliably. Some recent neurocognitive and functional/structural neuroimaging findings might be appropriate to use as SB indicators in the close future.Keywords: suicidal behavior, biological markers, serotonin, hopelessness, impulsivity, major depressive disorder, fMRI, PET, SPECT

  19. Neural bases of different cognitive strategies for facial affect processing in schizophrenia.

    Science.gov (United States)

    Fakra, Eric; Salgado-Pineda, Pilar; Delaveau, Pauline; Hariri, Ahmad R; Blin, Olivier

    2008-03-01

    To examine the neural basis and dynamics of facial affect processing in schizophrenic patients as compared to healthy controls. Fourteen schizophrenic patients and fourteen matched controls performed a facial affect identification task during fMRI acquisition. The emotional task included an intuitive emotional condition (matching emotional faces) and a more cognitively demanding condition (labeling emotional faces). Individual analysis for each emotional condition, and second-level t-tests examining both within-, and between-group differences, were carried out using a random effects approach. Psychophysiological interactions (PPI) were tested for variations in functional connectivity between amygdala and other brain regions as a function of changes in experimental conditions (labeling versus matching). During the labeling condition, both groups engaged similar networks. During the matching condition, schizophrenics failed to activate regions of the limbic system implicated in the automatic processing of emotions. PPI revealed an inverse functional connectivity between prefrontal regions and the left amygdala in healthy volunteers but there was no such change in patients. Furthermore, during the matching condition, and compared to controls, patients showed decreased activation of regions involved in holistic face processing (fusiform gyrus) and increased activation of regions associated with feature analysis (inferior parietal cortex, left middle temporal lobe, right precuneus). Our findings suggest that schizophrenic patients invariably adopt a cognitive approach when identifying facial affect. The distributed neocortical network observed during the intuitive condition indicates that patients may resort to feature-based, rather than configuration-based, processing and may constitute a compensatory strategy for limbic dysfunction.

  20. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  1. A Role of the Parasympathetic Nervous System in Cognitive Training.

    Science.gov (United States)

    Lin, Feng; Heffner, Kathi L; Ren, Ping; Tadin, Duje

    2017-01-01

    Vision-based speed of processing (VSOP) training can result in broad cognitive improvements in older adults with amnestic mild cognitive impairment (aMCI). What remains unknown, however, is what neurophysiological mechanisms account for the observed training effect. Much of the work in this area has focused on the central nervous system, neglecting the fact that the peripheral system can contributes to changes of the central nervous system and vice versa. We examined the prospective relationship between an adaptive parasympathetic nervous system response to cognitive stimuli and VSOP training-induced plasticity. Twenty-one participants with aMCI (10 for VSOP training, and 11 for mental leisure activities (MLA) control) were enrolled. We assessed high-frequency heart rate variability (HF-HRV) during training sessions, and striatum-related neural networks and cognition at baseline and post-training. Compared to MLA, the VSOP group showed a significant U-shaped pattern of HF-HRV response during training, as well as decreases in connectivity strength between bilateral striatal and prefrontal regions. These two effects were associated with training-induced improvements in both the trained (attention and processing speed) and transferred (working memory) cognitive domains. This work provides novel support for interactions between the central and the peripheral nervous systems in relation to cognitive training, and motivates further studies to elucidate the causality of the observed link. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Neural mechanisms underlying cognitive control of men with lifelong antisocial behavior.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Mu Ller, Bernhard; Forsting, Michael; Gizewski, Elke; Leygraf, Norbert; Hodgins, Sheilagh

    2014-04-30

    Results of meta-analyses suggested subtle deficits in cognitive control among antisocial individuals. Because almost all studies focused on children with conduct problems or adult psychopaths, however, little is known about cognitive control mechanisms among the majority of persistent violent offenders who present an antisocial personality disorder (ASPD). The present study aimed to determine whether offenders with ASPD, relative to non-offenders, display dysfunction in the neural mechanisms underlying cognitive control and to assess the extent to which these dysfunctions are associated with psychopathic traits and trait impulsivity. Participants comprised 21 violent offenders and 23 non-offenders who underwent event-related functional magnetic resonance imaging while performing a non-verbal Stroop task. The offenders, relative to the non-offenders, exhibited reduced response time interference and a different pattern of conflict- and error-related activity in brain areas involved in cognitive control, attention, language, and emotion processing, that is, the anterior cingulate, dorsolateral prefrontal, superior temporal and postcentral cortices, putamen, thalamus, and amygdala. Moreover, between-group differences in behavioural and neural responses revealed associations with core features of psychopathy and attentional impulsivity. Thus, the results of the present study confirmed the hypothesis that offenders with ASPD display alterations in the neural mechanisms underlying cognitive control and that those alterations relate, at least in part, to personality characteristics. Copyright © 2014. Published by Elsevier Ireland Ltd.

  3. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  4. Tuning down the hedonic brain: Cognitive load reduces neural responses to high-calorie food pictures in the nucleus accumbens.

    Science.gov (United States)

    van Dillen, Lotte F; van Steenbergen, Henk

    2018-06-01

    The present research examined whether cognitive load modulates the neural processing of appetitive, high-calorie food stimuli. In a functional magnetic resonance imaging (fMRI) study, participants quickly categorized high-calorie and low-calorie food pictures versus object pictures as edible or inedible while they concurrently performed a digit-span task that varied between low and high cognitive load (memorizing six digits vs. one digit). In line with predictions, the digit-span task engaged the dorsolateral prefrontal cortex (DLPFC) when cognitive load was high compared to low. Moreover, exposure to high-calorie compared to low-calorie food pictures led to increased activation in the nucleus accumbens (NAcc), but only when cognitive load was low and not when it was high. In addition, connectivity analyses showed that load altered the functional coupling between NAcc and right DLPFC during presentation of the high-calorie versus low-calorie food pictures. Together, these findings indicate that loading the cognitive system moderates hedonic brain responses to high-calorie food pictures via interactions between NAcc and DLPFC. Our findings are consistent with the putative cognitive nature of food motivation. Implications for future research are discussed.

  5. Music and Cognitive Development: From Notes to Neural Networks

    Science.gov (United States)

    Shore, Rebecca Ann

    2010-01-01

    This article investigates research on early childhood development and on both listening to music and participation in music activities by young children. Research is reviewed that explores possible relationships between various music-related experiences and cognitive development, from the "Mozart Effect" studies to participation in piano lessons…

  6. The Neural Circuitry of Expertise: Perceptual Learning and Social Cognition

    Directory of Open Access Journals (Sweden)

    Michael eHarre

    2013-12-01

    Full Text Available Amongst the most significant questions we are confronted with today include the integration of the brain's micro-circuitry, our ability to build the complex social networks that underpin society and how our society impacts on our ecological environment. In trying to unravel these issues one place to begin is at the level of the individual: to consider how we accumulate information about our environment, how this information leads to decisions and how our individual decisions in turn create our social environment. While this is an enormous task, we may already have at hand many of the tools we need. This article is intended to review some of the recent results in neuro-cognitive research and show how they can be extended to two very specific types of expertise: perceptual expertise and social cognition. These two cognitive skills span a vast range of our genetic heritage. Perceptual expertise developed very early in our evolutionary history and is likely a highly developed part of all mammals' cognitive ability. On the other hand social cognition is most highly developed in humans in that we are able to maintain larger and more stable long term social connections with more behaviourally diverse individuals than any other species. To illustrate these ideas I will discuss board games as a toy model of social interactions as they include many of the relevant concepts: perceptual learning, decision-making, long term planning and understanding the mental states of other people. Using techniques that have been developed in mathematical psychology, I show that we can represent some of the key features of expertise using stochastic differential equations. Such models demonstrate how an expert's long exposure to a particular context influences the information they accumulate in order to make a decision.These processes are not confined to board games, we are all experts in our daily lives through long exposure to the many regularities of daily tasks and

  7. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  8. Neural correlates of cognitive control in gambling disorder: a systematic review of fMRI studies.

    Science.gov (United States)

    Moccia, Lorenzo; Pettorruso, Mauro; De Crescenzo, Franco; De Risio, Luisa; di Nuzzo, Luigi; Martinotti, Giovanni; Bifone, Angelo; Janiri, Luigi; Di Nicola, Marco

    2017-07-01

    Decreased cognitive control over the urge to be involved in gambling activities is a core feature of Gambling Disorder (GD). Cognitive control can be differentiated into several cognitive sub-processes pivotal in GD clinical phenomenology, such as response inhibition, conflict monitoring, decision-making, and cognitive flexibility. This article aims to systematically review fMRI studies, which investigated the neural mechanisms underlying diminished cognitive control in GD. We conducted a comprehensive literature search and collected neuropsychological and neuroimaging data investigating cognitive control in GD. We included a total of 14 studies comprising 499 individuals. Our results indicate that impaired activity in prefrontal cortex may account for decreased cognitive control in GD, contributing to the progressive loss of control over gambling urges. Among prefrontal regions, orbital and ventromedial areas seem to be a possible nexus for sensory integration, value-based decision-making and emotional processing, thus contributing to both motivational and affective aspects of cognitive control. Finally, we discussed possible therapeutic approaches aimed at the restoration of cognitive control in GD, including pharmacological and brain stimulation treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  10. The neural bases of cognitive processes in gambling disorder

    OpenAIRE

    Potenza, Marc N.

    2014-01-01

    Functional imaging is offering powerful new tools to investigate the neurobiology of cognitive functioning in people with and without psychiatric conditions like gambling disorder. Based on similarities between gambling and substance-use disorders in neurocognitive and other domains, gambling disorder has recently been classified in DSM-5 as a behavioral addiction. Despite the advances in understanding, there exist multiple unanswered questions about the pathophysiology underlying gambling di...

  11. Erythropoietin modulates neural and cognitive processing of emotional information in biomarker models of antidepressant drug action in depressed patients

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr

    2010-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The curren...... study investigates the effects of Epo on the neural and cognitive response to emotional facial expressions in depressed patients.......Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The current...

  12. Neural correlates of focused attention and cognitive monitoring in meditation.

    Science.gov (United States)

    Manna, Antonietta; Raffone, Antonino; Perrucci, Mauro Gianni; Nardo, Davide; Ferretti, Antonio; Tartaro, Armando; Londei, Alessandro; Del Gratta, Cosimo; Belardinelli, Marta Olivetti; Romani, Gian Luca

    2010-04-29

    Meditation refers to a family of complex emotional and attentional regulatory practices, which can be classified into two main styles - focused attention (FA) and open monitoring (OM) - involving different attentional, cognitive monitoring and awareness processes. In a functional magnetic resonance study we originally characterized and contrasted FA and OM meditation forms within the same experiment, by an integrated FA-OM design. Theravada Buddhist monks, expert in both FA and OM meditation forms, and lay novices with 10 days of meditation practice, participated in the experiment. Our evidence suggests that expert meditators control cognitive engagement in conscious processing of sensory-related, thought and emotion contents, by massive self-regulation of fronto-parietal and insular areas in the left hemisphere, in a meditation state-dependent fashion. We also found that anterior cingulate and dorsolateral prefrontal cortices play antagonist roles in the executive control of the attention setting in meditation tasks. Our findings resolve the controversy between the hypothesis that meditative states are associated to transient hypofrontality or deactivation of executive brain areas, and evidence about the activation of executive brain areas in meditation. Finally, our study suggests that a functional reorganization of brain activity patterns for focused attention and cognitive monitoring takes place with mental practice, and that meditation-related neuroplasticity is crucially associated to a functional reorganization of activity patterns in prefrontal cortex and in the insula. Copyright 2010 Elsevier Inc. All rights reserved.

  13. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  14. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  15. Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia

    Science.gov (United States)

    Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.

    2012-10-01

    In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.

  16. Using Expert Systems To Build Cognitive Simulations.

    Science.gov (United States)

    Jonassen, David H.; Wang, Sherwood

    2003-01-01

    Cognitive simulations are runnable computer programs for modeling human cognitive activities. A case study is reported where expert systems were used as a formalism for modeling metacognitive processes in a seminar. Building cognitive simulations engages intensive introspection, ownership and meaning making in learners who build them. (Author/AEF)

  17. Multimodal neural correlates of cognitive control in the Human Connectome Project.

    Science.gov (United States)

    Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas; Kandala, Sridhar; Calhoun, Vince D; Barch, Deanna M

    2017-12-01

    Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions

  18. Cognitive abnormalities and neural mechanisms in post-traumatic stress disorder

    Directory of Open Access Journals (Sweden)

    Ting HU

    2017-10-01

    Full Text Available Post-traumatic stress disorder (PTSD is an anxiety disorder that develops usually in response to an overwhelmingly terrifying or a life-threatening event. The symptoms including intrusion, flashback, re-experiencing, hyperarousal and avoidance can seriously impair the cognitive functions. At present, the researches have found PTSD patients had the difficulty in retrieving autobiographical memory and narrative disorder, attention bias toward traumatic stimulus and intellectual decline. Decrease in hippocampus and amygdala's volumes, excess endoplasmic reticulum stress, medial prefrontal cortex's low activation and highly excited response of the amygdala to the traumatic stimulus may be the neural mechanisms of cognitive abnormalities. In- depth research on cognitive abnormalities provides directions for PTSD prevention and treatment, and the cognitive treatment by prolonged exposure and attention control may be the effective method. DOI: 10.11855/j.issn.0577-7402.2017.09.14

  19. The manipulative skill: Cognitive devices and their neural correlates underlying Machiavellian's decision making.

    Science.gov (United States)

    Bereczkei, Tamas

    2015-10-01

    Until now, Machiavellianism has mainly been studied in personality and social psychological framework, and little attention has been paid to the underlying cognitive and neural equipment. In light of recent findings, Machiavellian social skills are not limited to emotion regulation and "cold-mindedness" as many authors have recently stated, but linked to specific cognitive abilities. Although Machiavellians appear to have a relatively poor mindreading ability and emotional intelligence, they can efficiently exploit others which is likely to come from their flexible problem solving processes in changing environmental circumstances. The author proposed that Machiavellians have specialized cognitive domains of decision making, such as monitoring others' behavior, task orientation, reward seeking, inhibition of cooperative feelings, and choosing victims. He related the relevant aspects of cognitive functions to their neurological substrates, and argued why they make Machiavellians so successful in interpersonal relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Extent and neural basis of semantic memory impairment in mild cognitive impairment.

    Science.gov (United States)

    Barbeau, Emmanuel J; Didic, Mira; Joubert, Sven; Guedj, Eric; Koric, Lejla; Felician, Olivier; Ranjeva, Jean-Philippe; Cozzone, Patrick; Ceccaldi, Mathieu

    2012-01-01

    An increasing number of studies indicate that semantic memory is impaired in mild cognitive impairment (MCI). However, the extent and the neural basis of this impairment remain unknown. The aim of the present study was: 1) to evaluate whether all or only a subset of semantic domains are impaired in MCI patients; and 2) to assess the neural substrate of the semantic impairment in MCI patients using voxel-based analysis of MR grey matter density and SPECT perfusion. 29 predominantly amnestic MCI patients and 29 matched control subjects participated in this study. All subjects underwent a full neuropsychological assessment, along with a battery of five tests evaluating different domains of semantic memory. A semantic memory composite Z-score was established on the basis of this battery and was correlated with MRI grey matter density and SPECT perfusion measures. MCI patients were found to have significantly impaired performance across all semantic tasks, in addition to their anterograde memory deficit. Moreover, no temporal gradient was found for famous faces or famous public events and knowledge for the most remote decades was also impaired. Neuroimaging analyses revealed correlations between semantic knowledge and perirhinal/entorhinal areas as well as the anterior hippocampus. Therefore, the deficits in the realm of semantic memory in patients with MCI is more widespread than previously thought and related to dysfunction of brain areas beyond the limbic-diencephalic system involved in episodic memory. The severity of the semantic impairment may indicate a decline of semantic memory that began many years before the patients first consulted.

  1. Cognitive systems engineering in health care

    CERN Document Server

    Bisantz, Ann M; Fairbanks, Rollin J

    2014-01-01

    Cognitive Engineering for Better Health Care Systems, Ann M. Bisantz, Rollin J. Fairbanks, and Catherine M. BurnsThe Role of Cognitive Engineering in Improving Clinical Decision Support, Anne Miller and Laura MilitelloTeam Cognitive Work Analysis as an Approach for Understanding Teamwork in Health Care, Catherine M. BurnsCognitive Engineering Design of an Emergency Department Information System, Theresa K. Guarrera, Nicolette M. McGeorge, Lindsey N. Clark, David T. LaVergne, Zachary A. Hettinger, Rollin J. Fairbanks, and Ann M. BisantzDisplays for Health Care Teams: A Conceptual Framework and Design Methodology, Avi ParushInformation Modeling for Cognitive Work in a Health Care System, Priyadarshini R. PennathurSupport for ICU Clinician Cognitive Work through CSE, Christopher Nemeth, Shilo Anders, Jeffrey Brown, Anna Grome, Beth Crandall, and Jeremy PamplinMatching Cognitive Aids and the "Real Work" of Health Care in Support of Surgical Microsystem Teamwork, Sarah Henrickson Parker and Shawna J. PerryEngageme...

  2. Space Telecommunications Radio System STRS Cognitive Radio

    Science.gov (United States)

    Briones, Janette C.; Handler, Louis M.

    2013-01-01

    Radios today are evolving from awareness toward cognition. A software defined radio (SDR) provides the most capability for integrating autonomic decision making ability and allows the incremental evolution toward a cognitive radio. This cognitive radio technology will impact NASA space communications in areas such as spectrum utilization, interoperability, network operations, and radio resource management over a wide range of operating conditions. NASAs cognitive radio will build upon the infrastructure being developed by Space Telecommunication Radio System (STRS) SDR technology. This paper explores the feasibility of inserting cognitive capabilities in the NASA STRS architecture and the interfaces between the cognitive engine and the STRS radio. The STRS architecture defines methods that can inform the cognitive engine about the radio environment so that the cognitive engine can learn autonomously from experience, and take appropriate actions to adapt the radio operating characteristics and optimize performance.

  3. Cognitive logical systems with artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Liss, E

    1983-09-01

    The simulation of cognitive processes for the purpose of the technical development of learning systems with intelligent behavior is a basic object of the young interdisciplinary cognition science which is based upon artificial intelligence, cognitive psychology, computer science, linguistics and pedagogics. Cognitive systems may be described as knowledge-based logical systems. Based on structural and functional principles of intelligent automata and elementary information processing systems with structural learning capability the future process, machine and robot controls, advising units and fifth generation computers may be developed.

  4. Different neural and cognitive response to emotional faces in healthy monozygotic twins at risk of depression

    DEFF Research Database (Denmark)

    Miskowiak, K W; Glerup, L; Vestbo, C

    2015-01-01

    while performing a gender discrimination task. After the scan, they were given a faces dot-probe task, a facial expression recognition task and questionnaires assessing mood, personality traits and coping strategies. RESULTS: High-risk twins showed increased neural response to happy and fearful faces...... processing. These task-related changes in neural responses in high-risk twins were accompanied by impaired gender discrimination performance during face processing. They also displayed increased attention vigilance for fearful faces and were slower at recognizing facial expressions relative to low......BACKGROUND: Negative cognitive bias and aberrant neural processing of emotional faces are trait-marks of depression. Yet it is unclear whether these changes constitute an endophenotype for depression and are also present in healthy individuals with hereditary risk for depression. METHOD: Thirty...

  5. Neural effects of cognitive control load on auditory selective attention.

    Science.gov (United States)

    Sabri, Merav; Humphries, Colin; Verber, Matthew; Liebenthal, Einat; Binder, Jeffrey R; Mangalathu, Jain; Desai, Anjali

    2014-08-01

    Whether and how working memory disrupts or alters auditory selective attention is unclear. We compared simultaneous event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) responses associated with task-irrelevant sounds across high and low working memory load in a dichotic-listening paradigm. Participants performed n-back tasks (1-back, 2-back) in one ear (Attend ear) while ignoring task-irrelevant speech sounds in the other ear (Ignore ear). The effects of working memory load on selective attention were observed at 130-210ms, with higher load resulting in greater irrelevant syllable-related activation in localizer-defined regions in auditory cortex. The interaction between memory load and presence of irrelevant information revealed stronger activations primarily in frontal and parietal areas due to presence of irrelevant information in the higher memory load. Joint independent component analysis of ERP and fMRI data revealed that the ERP component in the N1 time-range is associated with activity in superior temporal gyrus and medial prefrontal cortex. These results demonstrate a dynamic relationship between working memory load and auditory selective attention, in agreement with the load model of attention and the idea of common neural resources for memory and attention. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. The neural bases of cognitive processes in gambling disorder.

    Science.gov (United States)

    Potenza, Marc N

    2014-08-01

    Functional imaging is offering powerful new tools to investigate the neurobiology of cognitive functioning in people with and without psychiatric conditions like gambling disorder. Based on similarities between gambling and substance-use disorders in neurocognitive and other domains, gambling disorder has recently been classified in the Diagnostic and Statistical Manual of Mental Disorders (5th edn) (DSM-5) as a behavioral addiction. Despite the advances in understanding, there exist multiple unanswered questions about the pathophysiology underlying gambling disorder and the promise for translating the neurobiological understanding into treatment advances remains largely unrealized. Here we review the neurocognitive underpinnings of gambling disorder with a view to improving prevention, treatment, and policy efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Cognitive object recognition system (CORS)

    Science.gov (United States)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  8. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

  9. Extended Cognition: Feedback Loops and Coupled Systems

    Directory of Open Access Journals (Sweden)

    Olga Markic

    2017-12-01

    Full Text Available The article explores two waves of active externalism. I first introduce the distinction between passive and active externalism and analyse a proposal of active externalism based on the principle of parity proposed by Clark and Chalmers. There are two main obstacles, causal-constitution fallacy and cognitive bloat, that threaten the extended cognition hypothesis. The second wave of discussions based on the complementarity principle deals with cognitive systems with feedback loops between internal and external elements and is a more radical departure from functionalism and traditional thinking about cognition. I conclude with some remarks on potential ethical considerations of extended cognition.

  10. Neural Correlates for Intrinsic Motivational Deficits of Schizophrenia; Implications for Therapeutics of Cognitive Impairment

    Science.gov (United States)

    Takeda, Kazuyoshi; Sumiyoshi, Tomiki; Matsumoto, Madoka; Murayama, Kou; Ikezawa, Satoru; Matsumoto, Kenji; Nakagome, Kazuyuki

    2018-01-01

    The ultimate goal of the treatment of schizophrenia is recovery, a notion related to improvement of cognitive and social functioning. Cognitive remediation therapies (CRT), one of the most effective cognition enhancing methods, have been shown to moderately improve social functioning. For this purpose, intrinsic motivation, related to internal values such as interest and enjoyment, has been shown to play a key role. Although the impairment of intrinsic motivation is one of the characteristics of schizophrenia, its neural mechanisms remain unclear. This is related to the lack of feasible measures of intrinsic motivation, and its response to treatment. According to the self-determination theory (SDT), not only intrinsic motivation, but extrinsic motivation has been reported to enhance learning and memory in healthy subjects to some extent. This finding suggests the contribution of different types of motivation to potentiate the ability of the CRT to treat cognitive impairment of schizophrenia. In this paper, we provide a review of psychological characteristics, assessment methods, and neural correlates of intrinsic motivation in healthy subjects and patients with schizophrenia. Particularly, we focus on neuroimaging studies of intrinsic motivation, including our own. These considerations are relevant to enhancement of functional outcomes of schizophrenia. PMID:29922185

  11. Neural correlates of cognitive dissonance and choice-induced preference change.

    Science.gov (United States)

    Izuma, Keise; Matsumoto, Madoka; Murayama, Kou; Samejima, Kazuyuki; Sadato, Norihiro; Matsumoto, Kenji

    2010-12-21

    According to many modern economic theories, actions simply reflect an individual's preferences, whereas a psychological phenomenon called "cognitive dissonance" claims that actions can also create preference. Cognitive dissonance theory states that after making a difficult choice between two equally preferred items, the act of rejecting a favorite item induces an uncomfortable feeling (cognitive dissonance), which in turn motivates individuals to change their preferences to match their prior decision (i.e., reducing preference for rejected items). Recently, however, Chen and Risen [Chen K, Risen J (2010) J Pers Soc Psychol 99:573-594] pointed out a serious methodological problem, which casts a doubt on the very existence of this choice-induced preference change as studied over the past 50 y. Here, using a proper control condition and two measures of preferences (self-report and brain activity), we found that the mere act of making a choice can change self-report preference as well as its neural representation (i.e., striatum activity), thus providing strong evidence for choice-induced preference change. Furthermore, our data indicate that the anterior cingulate cortex and dorsolateral prefrontal cortex tracked the degree of cognitive dissonance on a trial-by-trial basis. Our findings provide important insights into the neural basis of how actions can alter an individual's preferences.

  12. Neural Correlates for Intrinsic Motivational Deficits of Schizophrenia; Implications for Therapeutics of Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Kazuyoshi Takeda

    2018-06-01

    Full Text Available The ultimate goal of the treatment of schizophrenia is recovery, a notion related to improvement of cognitive and social functioning. Cognitive remediation therapies (CRT, one of the most effective cognition enhancing methods, have been shown to moderately improve social functioning. For this purpose, intrinsic motivation, related to internal values such as interest and enjoyment, has been shown to play a key role. Although the impairment of intrinsic motivation is one of the characteristics of schizophrenia, its neural mechanisms remain unclear. This is related to the lack of feasible measures of intrinsic motivation, and its response to treatment. According to the self-determination theory (SDT, not only intrinsic motivation, but extrinsic motivation has been reported to enhance learning and memory in healthy subjects to some extent. This finding suggests the contribution of different types of motivation to potentiate the ability of the CRT to treat cognitive impairment of schizophrenia. In this paper, we provide a review of psychological characteristics, assessment methods, and neural correlates of intrinsic motivation in healthy subjects and patients with schizophrenia. Particularly, we focus on neuroimaging studies of intrinsic motivation, including our own. These considerations are relevant to enhancement of functional outcomes of schizophrenia.

  13. Cognitive and Neural Correlates of Mathematical Giftedness in Adults and Children: A Review

    Directory of Open Access Journals (Sweden)

    Timothy Myers

    2017-10-01

    Full Text Available Most mathematical cognition research has focused on understanding normal adult function and child development as well as mildly and moderately impaired mathematical skill, often labeled developmental dyscalculia and/or mathematical learning disability. In contrast, much less research is available on cognitive and neural correlates of gifted/excellent mathematical knowledge in adults and children. In order to facilitate further inquiry into this area, here we review 40 available studies, which examine the cognitive and neural basis of gifted mathematics. Studies associated a large number of cognitive factors with gifted mathematics, with spatial processing and working memory being the most frequently identified contributors. However, the current literature suffers from low statistical power, which most probably contributes to variability across findings. Other major shortcomings include failing to establish domain and stimulus specificity of findings, suggesting causation without sufficient evidence and the frequent use of invalid backward inference in neuro-imaging studies. Future studies must increase statistical power and neuro-imaging studies must rely on supporting behavioral data when interpreting findings. Studies should investigate the factors shown to correlate with math giftedness in a more specific manner and determine exactly how individual factors may contribute to gifted math ability.

  14. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  15. LOGIC WITH EXCEPTION ON THE ALGEBRA OF FOURIER-DUAL OPERATIONS: NEURAL NET MECHANISM OF COGNITIVE DISSONANCE REDUCING

    Directory of Open Access Journals (Sweden)

    A. V. Pavlov

    2014-01-01

    Full Text Available A mechanism of cognitive dissonance reducing is demonstrated with approach for non-monotonic fuzzy-valued logics by Fourier-holography technique implementation developing. Cognitive dissonance occurs under perceiving of new information that contradicts to the existing subjective pattern of the outside world, represented by double Fourier-transform cascade with a hologram – neural layers interconnections matrix of inner information representation and logical conclusion. The hologram implements monotonic logic according to “General Modus Ponens” rule. New information is represented by a hologram of exclusion that implements interconnections of logical conclusion and exclusion for neural layers. The latter are linked by Fourier transform that determines duality of the algebra forming operations of conjunction and disjunction. Hologram of exclusion forms conclusion that is dual to the “General Modus Ponens” conclusion. It is shown, that trained for the main rule and exclusion system can be represented by two-layered neural network with separate interconnection matrixes for direct and inverse iterations. The network energy function is involved determining the cyclic dynamics character; dissipative factor causing convergence type of the dynamics is analyzed. Both “General Modus Ponens” and exclusion holograms recording conditions on the dynamics and convergence of the system are demonstrated. The system converges to a stable status, in which logical conclusion doesn’t depend on the inner information. Such kind of dynamics, leading to tolerance forming, is typical for ordinary kind of thinking, aimed at inner pattern of outside world stability. For scientific kind of thinking, aimed at adequacy of the inner pattern of the world, a mechanism is needed to stop the net relaxation; the mechanism has to be external relative to the model of logic. Computer simulation results for the learning conditions adequate to real holograms recording are

  16. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  17. Evidence for a neural dual-process account for adverse effects of cognitive control.

    Science.gov (United States)

    Zink, Nicolas; Stock, Ann-Kathrin; Colzato, Lorenza; Beste, Christian

    2018-06-09

    Advantageous effects of cognitive control are well-known, but cognitive control may also have adverse effects, for example when it suppresses the implicit processing of stimulus-response (S-R) bindings that could benefit task performance. Yet, the neurophysiological and functional neuroanatomical structures associated with adverse effects of cognitive control are poorly understood. We used an extreme group approach to compare individuals who exhibit adverse effects of cognitive control to individuals who do not by combining event-related potentials (ERPs), source localization, time-frequency analysis and network analysis methods. While neurophysiological correlates of cognitive control (i.e. N2, N450, theta power and theta-mediated neuronal network efficiency) and task-set updating (P3) both reflect control demands and implicit information processing, differences in the degree of adverse cognitive control effects are associated with two independent neural mechanisms: Individuals, who show adverse behavioral effects of cognitive control, show reduced small-world properties and thus reduced efficiency in theta-modulated networks when they fail to effectively process implicit information. In contrast to this, individuals who do not display adverse control effects show enhanced task-set updating mechanism when effectively processing implicit information, which is reflected by the P3 ERP component and associated with the temporo-parietal junction (TPJ, BA 40) and medial frontal gyrus (MFG; BA 8). These findings suggest that implicit S-R contingencies, which benefit response selection without cognitive control, are always 'picked up', but may fail to be integrated with task representations to guide response selection. This provides evidence for a neurophysiological and functional neuroanatomical "dual-process" account of adverse cognitive control effects.

  18. Neural Correlates of Cognitive Intervention in Persons at Risk of Developing Alzheimer’s disease

    Directory of Open Access Journals (Sweden)

    SM Hadi eHosseini

    2014-08-01

    Full Text Available Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD. There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.

  19. Neural correlates of cognitive intervention in persons at risk of developing Alzheimer’s disease

    Science.gov (United States)

    Hosseini, S. M. Hadi; Kramer, Joel H.; Kesler, Shelli R.

    2014-01-01

    Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training. PMID:25206335

  20. Neural Correlates of Changes in a Visual Search Task due to Cognitive Training in Seniors

    Directory of Open Access Journals (Sweden)

    Nele Wild-Wall

    2012-01-01

    Full Text Available This study aimed to elucidate the underlying neural sources of near transfer after a multidomain cognitive training in older participants in a visual search task. Participants were randomly assigned to a social control, a no-contact control and a training group, receiving a 4-month paper-pencil and PC-based trainer guided cognitive intervention. All participants were tested in a before and after session with a conjunction visual search task. Performance and event-related potentials (ERPs suggest that the cognitive training improved feature processing of the stimuli which was expressed in an increased rate of target detection compared to the control groups. This was paralleled by enhanced amplitudes of the frontal P2 in the ERP and by higher activation in lingual and parahippocampal brain areas which are discussed to support visual feature processing. Enhanced N1 and N2 potentials in the ERP for nontarget stimuli after cognitive training additionally suggest improved attention and subsequent processing of arrays which were not immediately recognized as targets. Possible test repetition effects were confined to processes of stimulus categorisation as suggested by the P3b potential. The results show neurocognitive plasticity in aging after a broad cognitive training and allow pinpointing the functional loci of effects induced by cognitive training.

  1. Extended Cognitive System and Epistemic Subject

    Directory of Open Access Journals (Sweden)

    Trybulec Barbara

    2015-03-01

    Full Text Available The concept of an extended cognitive system is central to contemporary studies of cognition. In the paper I analyze the place of the epistemic subject within the extended cognitive system. Is it extended as well? In answering this question I focus on the differences between the first and the second wave of arguments for the extended mind thesis. I argue that the position of Cognitive Integration represented by Richard Menary is much more intuitive and fruitful in analyses of cognition and knowledge than the early argument formulated by Andy Clark and David Chalmers. Cognitive Integration is compatible with virtue epistemology of John Greco’s agent reliabilism. The epistemic subject is constituted by its cognitive character composed of an integrated set of cognitive abilities and processes. Some of these processes are extended, they are a manipulation of external informational structures and, as such, they constitute epistemic practices. Epistemic practices are normative; to conduct them correctly the epistemic subject needs to obey epistemic norms embedded in the cultural context. The epistemic subject is not extended because of the casual coupling with external informational artifacts which extend his mind from inside the head and into the world. Rather, cognitive practices constitute the subject’s mind, they transform his cognitive abilities, and this is what makes the mind and epistemic subject “extended”.

  2. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  3. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  4. The neural correlates of cognitive reappraisal during emotional autobiographical memory recall.

    Science.gov (United States)

    Holland, Alisha C; Kensinger, Elizabeth A

    2013-01-01

    We used fMRI to investigate the neural processes engaged as individuals down- and up-regulated the emotions associated with negative autobiographical memories (AMs) using cognitive reappraisal strategies. Our analyses examined neural activity during three separate phases, as participants (a) viewed a reappraisal instruction (i.e., Decrease, Increase, Maintain), (b) searched for an AM referenced by a self-generated cue, and (c) elaborated upon the details of the AM being held in mind. Decreasing emotional intensity primarily engaged activity in regions previously implicated in cognitive control (e.g., dorsal and ventral lateral pFC), emotion generation and processing (e.g., amygdala, insula), and visual imagery (e.g., precuneus) as participants searched for and retrieved events. In contrast, increasing emotional intensity engaged similar regions during the instruction phase (i.e., before a memory cue was presented) and again as individuals later elaborated upon the details of the events they had recalled. These findings confirm that reappraisal can modulate neural activity during the recall of personally relevant events, although the time course of this modulation appears to depend on whether individuals are attempting to down- or up-regulate their emotions.

  5. Neural correlates of saccadic inhibition in healthy elderly and patients with amnestic mild cognitive impairment

    Science.gov (United States)

    Alichniewicz, K. K.; Brunner, F.; Klünemann, H. H.; Greenlee, M. W.

    2013-01-01

    Performance on tasks that require saccadic inhibition declines with age and altered inhibitory functioning has also been reported in patients with Alzheimer's disease. Although mild cognitive impairment (MCI) is assumed to be a high-risk factor for conversion to AD, little is known about changes in saccadic inhibition and its neural correlates in this condition. Our study determined whether the neural activation associated with saccadic inhibition is altered in persons with amnestic mild cognitive impairment (aMCI). Functional magnetic resonance imaging (fMRI) revealed decreased activation in parietal lobe in healthy elderly persons compared to young persons and decreased activation in frontal eye fields in aMCI patients compared to healthy elderly persons during the execution of anti-saccades. These results illustrate that the decline in inhibitory functions is associated with impaired frontal activation in aMCI. This alteration in function might reflect early manifestations of AD and provide new insights in the neural activation changes that occur in pathological ageing. PMID:23898312

  6. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  7. Neural Systems Underlying Emotional and Non-emotional Interference Processing: An ALE Meta-Analysis of Functional Neuroimaging Studies

    OpenAIRE

    Xu, Min; Xu, Guiping; Yang, Yang

    2016-01-01

    Understanding how the nature of interference might influence the recruitments of the neural systems is considered as the key to understanding cognitive control. Although, interference processing in the emotional domain has recently attracted great interest, the question of whether there are separable neural patterns for emotional and non-emotional interference processing remains open. Here, we performed an activation likelihood estimation meta-analysis of 78 neuroimaging experiments, and exam...

  8. Amyloid-independent functional neural correlates of episodic memory in amnestic mild cognitive impairment.

    Science.gov (United States)

    Seo, Eun Hyun; Choo, I L Han

    2016-06-01

    Although amnestic mild cognitive impairment (aMCI) could have various biological characteristics, little attention has been given to the nature of episodic memory decline in aMCI with pathophysiologies other than Alzheimer's disease (AD), i.e., aMCI with low beta-amyloid (Aβ) burden. This study aimed to identify the functional neural basis of episodic memory impairment in aMCI with Aβ burden negative (aMCI-Aβ-) and to compare these results with aMCI with Aβ burden positive (aMCI-Aβ+). Individuals with aMCI (n = 498) were selected from the Alzheimer's Disease Neuroimaging Initiative database. Based on the mean florbetapir standard uptake value ratio, participants were classified as aMCI-Aβ- or aMCI-Aβ+. Correlations between memory scores and regional cerebral glucose metabolism (rCMglc) were analyzed separately for the two subgroups using a multiple regression model. For aMCI-Aβ-, significant positive correlations between memory and rCMglc were found in the bilateral claustrum, right thalamus, left anterior cingulate cortex, left insula, and right posterior cingulate. For aMCI-Aβ+, significant positive correlations between memory and rCMglc were found in the temporoparietal areas. These correlation patterns remained unchanged when clinical severity was added as a covariate Our findings indicate that memory impairment in aMCI-Aβ- is related to multimodal integrative processing and the attentional control system, whereas memory impairment in aMCI-Aβ+ is related to the typical brain memory systems and AD signature. These results suggest that although the two subgroups are clinically in the same category as aMCI, the memory impairment process depends on completely different functional brain regions according to their Aβ burden level.

  9. Cognitive and neural consequences of memory suppression in major depressive disorder.

    Science.gov (United States)

    Sacchet, Matthew D; Levy, Benjamin J; Hamilton, J Paul; Maksimovskiy, Arkadiy; Hertel, Paula T; Joormann, Jutta; Anderson, Michael C; Wagner, Anthony D; Gotlib, Ian H

    2017-02-01

    Negative biases in cognition have been documented consistently in major depressive disorder (MDD), including difficulties in the ability to control the processing of negative material. Although negative information-processing biases have been studied using both behavioral and neuroimaging paradigms, relatively little research has been conducted examining the difficulties of depressed persons with inhibiting the retrieval of negative information from long-term memory. In this study, we used the think/no-think paradigm and functional magnetic resonance imaging to assess the cognitive and neural consequences of memory suppression in individuals diagnosed with depression and in healthy controls. The participants showed typical behavioral forgetting effects, but contrary to our hypotheses, there were no differences between the depressed and nondepressed participants or between neutral and negative memories. Relative to controls, depressed individuals exhibited greater activity in right middle frontal gyrus during memory suppression, regardless of the valence of the suppressed stimuli, and differential activity in the amygdala and hippocampus during memory suppression involving negatively valenced stimuli. These findings indicate that depressed individuals are characterized by neural anomalies during the suppression of long-term memories, increasing our understanding of the brain bases of negative cognitive biases in MDD.

  10. Neural activity related to cognitive and emotional empathy in post-traumatic stress disorder.

    Science.gov (United States)

    Mazza, Monica; Tempesta, Daniela; Pino, Maria Chiara; Nigri, Anna; Catalucci, Alessia; Guadagni, Veronica; Gallucci, Massimo; Iaria, Giuseppe; Ferrara, Michele

    2015-04-01

    The aim of this study is to evaluate the empathic ability and its functional brain correlates in post-traumatic stress disorder subjects (PTSD). Seven PTSD subjects and ten healthy controls, all present in the L'Aquila area during the earthquake of the April 2009, underwent fMRI during which they performed a modified version of the Multifaceted Empathy Test. PTSD patients showed impairments in implicit and explicit emotional empathy, but not in cognitive empathy. Brain responses during cognitive empathy showed an increased activation in patients compared to controls in the right medial frontal gyrus and the left inferior frontal gyrus. During implicit emotional empathy responses patients with PTSD, compared to controls, exhibited greater neural activity in the left pallidum and right insula; instead the control group showed an increased activation in right inferior frontal gyrus. Finally, in the explicit emotional empathy responses the PTSD group showed a reduced neural activity in the left insula and the left inferior frontal gyrus. The behavioral deficit limited to the emotional empathy dimension, accompanied by different patterns of activation in empathy related brain structures, represent a first piece of evidence of a dissociation between emotional and cognitive empathy in PTSD patients. The present findings support the idea that empathy is a multidimensional process, with different facets depending on distinct anatomical substrates. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Pushing the Limits: Cognitive, Affective, & Neural Plasticity Revealed by an Intensive Multifaceted Intervention

    Directory of Open Access Journals (Sweden)

    Michael David Mrazek

    2016-03-01

    Full Text Available Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed.

  12. Adaptive OFDM System Design For Cognitive Radio

    NARCIS (Netherlands)

    Zhang, Q.; Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria

    2006-01-01

    Recently, Cognitive Radio has been proposed as a promising technology to improve spectrum utilization. A highly flexible OFDM system is considered to be a good candidate for the Cognitive Radio baseband processing where individual carriers can be switched off for frequencies occupied by a licensed

  13. Distinct neural substrates of affective and cognitive theory of mind impairment in semantic dementia.

    Science.gov (United States)

    Bejanin, Alexandre; Chételat, Gaël; Laisney, Mickael; Pélerin, Alice; Landeau, Brigitte; Merck, Catherine; Belliard, Serge; de La Sayette, Vincent; Eustache, Francis; Desgranges, Béatrice

    2017-06-01

    Using structural MRI, we investigated the brain substrates of both affective and cognitive theory of mind (ToM) in 19 patients with semantic dementia. We also ran intrinsic connectivity analyses to identify the networks to which the substrates belong and whether they are functionally disturbed in semantic dementia. In line with previous studies, we observed a ToM impairment in patients with semantic dementia even when semantic memory was regressed out. Our results also highlighted different neural bases according to the nature (affective or cognitive) of the representations being inferred. The affective ToM deficit was associated with atrophy in the amygdala, suggesting the involvement of emotion-processing deficits in this impairment. By contrast, cognitive ToM performances were correlated with the volume of medial prefrontal and parietal regions, as well as the right frontal operculum. Intrinsic connectivity analyses revealed decreased functional connectivity, mainly between midline cortical regions and temporal regions. They also showed that left medial temporal regions were functionally isolated, a further possible hindrance to normal social cognitive functioning in semantic dementia. Overall, this study addressed for the first time the neuroanatomical substrates of both cognitive and affective ToM disruption in semantic dementia, highlighting disturbed connectivity within the networks that sustain these abilities.

  14. Executive functions in mild cognitive impairment: emergence and breakdown of neural plasticity.

    Science.gov (United States)

    Clément, Francis; Gauthier, Serge; Belleville, Sylvie

    2013-05-01

    Our goal was to test the effect of disease severity on the brain activation associated with two executive processes: manipulation and divided attention. This was achieved by administrating a manipulation task and a divided attention task using functional magnetic resonance imaging to 24 individuals with mild cognitive impairment (MCI) and 14 healthy controls matched for age, sex and education. The Mattis Dementia Rating Scale was used to divide persons with MCI into those with better and worse cognitive performances. Both tasks were associated with more brain activation in the MCI group with higher cognition than in healthy controls, particularly in the left frontal areas. Correlational analyses indicated that greater activation in a frontostriatal network hyperactivated by the higher-cognition group was related with better task performance, suggesting that these activations may support functional reorganization of a compensatory nature. By contrast, the lower-cognition group failed to show greater cerebral hyperactivation than controls during the divided attention task and, during the manipulation task, and showed less brain activation than controls in the left ventrolateral cortex, a region commonly hypoactivated in patients with Alzheimer's disease. These findings indicate that, during the early phase of MCI, executive functioning benefits from neural reorganization, but that a breakdown of this brain plasticity characterizes the late stages of MCI. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Neural Correlates of Hostile Jokes: Cognitive and Motivational Processes in Humor Appreciation

    Directory of Open Access Journals (Sweden)

    Yu-Chen Chan

    2016-10-01

    Full Text Available Hostile jokes provide aggressive catharsis and a feeling of superiority. Behavioral research has found that hostile jokes are perceived as funnier than non-hostile jokes. The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing hostile jokes (HJs, non-hostile jokes (NJs, and their corresponding hostile sentences (HSs and non-hostile sentences (NSs. Hostile jokes primarily showed activation in the dorsomedial prefrontal cortex (dmPFC and midbrain compared with the corresponding hostile baseline. Conversely, non-hostile jokes primarily revealed activation in the ventromedial PFC (vmPFC, amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc compared with the corresponding non-hostile baseline. These results support the critical role of the medial prefrontal cortex (mPFC for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of hostile jokes showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of non-hostile jokes displayed increased activation in the vmPFC, which suggested social-affective engagement. Hostile jokes versus non-hostile jokes primarily showed increased activation in the dmPFC and midbrain, whereas non-hostile jokes versus hostile jokes primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for hostile jokes and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for non-hostile jokes. Surprisingly, the neural correlates of hostile jokes were not perceived as funnier than non-hostile jokes. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation

  16. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  17. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  18. Cognitive flexibility modulates maturation and music-training-related changes in neural sound discrimination.

    Science.gov (United States)

    Saarikivi, Katri; Putkinen, Vesa; Tervaniemi, Mari; Huotilainen, Minna

    2016-07-01

    Previous research has demonstrated that musicians show superior neural sound discrimination when compared to non-musicians, and that these changes emerge with accumulation of training. Our aim was to investigate whether individual differences in executive functions predict training-related changes in neural sound discrimination. We measured event-related potentials induced by sound changes coupled with tests for executive functions in musically trained and non-trained children aged 9-11 years and 13-15 years. High performance in a set-shifting task, indexing cognitive flexibility, was linked to enhanced maturation of neural sound discrimination in both musically trained and non-trained children. Specifically, well-performing musically trained children already showed large mismatch negativity (MMN) responses at a young age as well as at an older age, indicating accurate sound discrimination. In contrast, the musically trained low-performing children still showed an increase in MMN amplitude with age, suggesting that they were behind their high-performing peers in the development of sound discrimination. In the non-trained group, in turn, only the high-performing children showed evidence of an age-related increase in MMN amplitude, and the low-performing children showed a small MMN with no age-related change. These latter results suggest an advantage in MMN development also for high-performing non-trained individuals. For the P3a amplitude, there was an age-related increase only in the children who performed well in the set-shifting task, irrespective of music training, indicating enhanced attention-related processes in these children. Thus, the current study provides the first evidence that, in children, cognitive flexibility may influence age-related and training-related plasticity of neural sound discrimination. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  19. Neural Basis of Cognitive Assessment in Alzheimer Disease, Amnestic Mild Cognitive Impairment, and Subjective Memory Complaints.

    Science.gov (United States)

    Matías-Guiu, Jordi A; Cabrera-Martín, María Nieves; Valles-Salgado, María; Pérez-Pérez, Alicia; Rognoni, Teresa; Moreno-Ramos, Teresa; Carreras, José Luis; Matías-Guiu, Jorge

    2017-07-01

    Interpreting cognitive tests is often challenging. The same test frequently examines multiple cognitive functions, and the functional and anatomical basis underlying test performance is unknown in many cases. This study analyses the correlation of different neuropsychological test results with brain metabolism in a series of patients evaluated for suspected Alzheimer disease. 20 healthy controls and 80 patients consulting for memory loss were included, in which cognitive study and 18 F-fluorodeoxyglucose PET were performed. Patients were categorized according to Reisberg's Global Deterioration Scale. Voxel-based analysis was used to determine correlations between brain metabolism and performance on the following tests: Free and Cued Selective Reminding Test (FCSRT), Boston Naming Test (BNT), Trail Making Test, Rey-Osterrieth Complex Figure test, Visual Object and Space Perception Battery (VOSP), and Tower of London (ToL) test. Mean age in the patient group was 73.9 ± 10.6 years, and 47 patients were women (58.7%). FCSRT findings were positively correlated with metabolism in the medial and anterior temporal region bilaterally, the left precuneus, and posterior cingulate. BNT results were correlated with metabolism in the middle temporal, superior, fusiform, and frontal medial gyri bilaterally. VOSP results were related to the occipital and parietotemporal regions bilaterally. ToL scores were correlated to metabolism in the right temporoparietal and frontal regions. These results suggest that different areas of the brain are involved in the processes required to complete different cognitive tests. Ascertaining the functional basis underlying these tests may prove helpful for understanding and interpreting them. Copyright © 2017 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  20. Neural correlates of true and false memory in mild cognitive impairment.

    Science.gov (United States)

    Sweeney-Reed, Catherine M; Riddell, Patricia M; Ellis, Judi A; Freeman, Jayne E; Nasuto, Slawomir J

    2012-01-01

    The goal of this research was to investigate the changes in neural processing in mild cognitive impairment. We measured phase synchrony, amplitudes, and event-related potentials in veridical and false memory to determine whether these differed in participants with mild cognitive impairment compared with typical, age-matched controls. Empirical mode decomposition phase locking analysis was used to assess synchrony, which is the first time this analysis technique has been applied in a complex cognitive task such as memory processing. The technique allowed assessment of changes in frontal and parietal cortex connectivity over time during a memory task, without a priori selection of frequency ranges, which has been shown previously to influence synchrony detection. Phase synchrony differed significantly in its timing and degree between participant groups in the theta and alpha frequency ranges. Timing differences suggested greater dependence on gist memory in the presence of mild cognitive impairment. The group with mild cognitive impairment had significantly more frontal theta phase locking than the controls in the absence of a significant behavioural difference in the task, providing new evidence for compensatory processing in the former group. Both groups showed greater frontal phase locking during false than true memory, suggesting increased searching when no actual memory trace was found. Significant inter-group differences in frontal alpha phase locking provided support for a role for lower and upper alpha oscillations in memory processing. Finally, fronto-parietal interaction was significantly reduced in the group with mild cognitive impairment, supporting the notion that mild cognitive impairment could represent an early stage in Alzheimer's disease, which has been described as a 'disconnection syndrome'.

  1. Neural Correlates of True and False Memory in Mild Cognitive Impairment

    Science.gov (United States)

    Sweeney-Reed, Catherine M.; Riddell, Patricia M.; Ellis, Judi A.; Freeman, Jayne E.; Nasuto, Slawomir J.

    2012-01-01

    The goal of this research was to investigate the changes in neural processing in mild cognitive impairment. We measured phase synchrony, amplitudes, and event-related potentials in veridical and false memory to determine whether these differed in participants with mild cognitive impairment compared with typical, age-matched controls. Empirical mode decomposition phase locking analysis was used to assess synchrony, which is the first time this analysis technique has been applied in a complex cognitive task such as memory processing. The technique allowed assessment of changes in frontal and parietal cortex connectivity over time during a memory task, without a priori selection of frequency ranges, which has been shown previously to influence synchrony detection. Phase synchrony differed significantly in its timing and degree between participant groups in the theta and alpha frequency ranges. Timing differences suggested greater dependence on gist memory in the presence of mild cognitive impairment. The group with mild cognitive impairment had significantly more frontal theta phase locking than the controls in the absence of a significant behavioural difference in the task, providing new evidence for compensatory processing in the former group. Both groups showed greater frontal phase locking during false than true memory, suggesting increased searching when no actual memory trace was found. Significant inter-group differences in frontal alpha phase locking provided support for a role for lower and upper alpha oscillations in memory processing. Finally, fronto-parietal interaction was significantly reduced in the group with mild cognitive impairment, supporting the notion that mild cognitive impairment could represent an early stage in Alzheimer’s disease, which has been described as a ‘disconnection syndrome’. PMID:23118992

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  3. Developing a 3-choice serial reaction time task for examining neural and cognitive function in an equine model.

    Science.gov (United States)

    Roberts, Kirsty; Hemmings, Andrew J; McBride, Sebastian D; Parker, Matthew O

    2017-12-01

    Large animal models of human neurological disorders are advantageous compared to rodent models due to their neuroanatomical complexity, longevity and their ability to be maintained in naturalised environments. Some large animal models spontaneously develop behaviours that closely resemble the symptoms of neural and psychiatric disorders. The horse is an example of this; the domestic form of this species consistently develops spontaneous stereotypic behaviours akin to the compulsive and impulsive behaviours observed in human neurological disorders such as Tourette's syndrome. The ability to non-invasively probe normal and abnormal equine brain function through cognitive testing may provide an extremely useful methodological tool to assess brain changes associated with certain human neurological and psychiatric conditions. An automated operant system with the ability to present visual and auditory stimuli as well as dispense salient food reward was developed. To validate the system, ten horses were trained and tested using a standard cognitive task (three choice serial reaction time task (3-CSRTT)). All animals achieved total learning criterion and performed six probe sessions. Learning criterion was met within 16.30±0.79 sessions over a three day period. During six probe sessions, level of performance was maintained at 80.67±0.57% (mean±SEM) accuracy. This is the first mobile fully automated system developed to examine cognitive function in the horse. A fully-automated operant system for mobile cognitive function of a large animal model has been designed and validated. Horses pose an interesting complementary model to rodents for the examination of human neurological dysfunction. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  5. Contribution Towards Practical Cognitive Radios Systems

    KAUST Repository

    Ben Ghorbel, Mahdi

    2013-01-01

    to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum

  6. Narrative Cognition in Interactive Systems

    DEFF Research Database (Denmark)

    Bruni, Luis Emilio; Baceviciute, Sarune; Arief, Mohammed

    2014-01-01

    In this article we explore some of the methodological problems related to characterizing cognitive aspects of involvement with interactive narratives using well known EEG/ERP techniques. To exemplify this, we construct an experimental EEG-ERP set-up with an interactive narrative that considers th...

  7. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

    In this paper, we investigate the dynamical behaviors of neural excitable system without periodic external current (proposed by Chialvo [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] and with periodic external current as system's parameters vary. The existence and stability of three fixed points, bifurcation of fixed points, the conditions of existences of fold bifurcation, flip bifurcation and Hopf bifurcation are derived by using bifurcation theory and center manifold theorem. The chaotic existence in the sense of Marotto's definition of chaos is proved. We then give the numerical simulated results (using bifurcation diagrams, computations of Maximum Lyapunov exponent and phase portraits), which not only show the consistence with the analytic results but also display new and interesting dynamical behaviors, including the complete period-doubling and inverse period-doubling bifurcation, symmetry period-doubling bifurcations of period-3 orbit, simultaneous occurrence of two different routes (invariant cycle and period-doubling bifurcations) to chaos for a given bifurcation parameter, sudden disappearance of chaos at one critical point, a great abundance of period windows (period 2 to 10, 12, 19, 20 orbits, and so on) in transient chaotic regions with interior crises, strange chaotic attractors and strange non-chaotic attractor. In particular, the parameter k plays a important role in the system, which can leave the chaotic behavior or the quasi-periodic behavior to period-1 orbit as k varies, and it can be considered as an control strategy of chaos by adjusting the parameter k. Combining the existing results in [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] with the new results reported in this paper, a more complete description of the system is now obtained

  8. Engagement with the auditory processing system during targeted auditory cognitive training mediates changes in cognitive outcomes in individuals with schizophrenia.

    Science.gov (United States)

    Biagianti, Bruno; Fisher, Melissa; Neilands, Torsten B; Loewy, Rachel; Vinogradov, Sophia

    2016-11-01

    Individuals with schizophrenia who engage in targeted cognitive training (TCT) of the auditory system show generalized cognitive improvements. The high degree of variability in cognitive gains maybe due to individual differences in the level of engagement of the underlying neural system target. 131 individuals with schizophrenia underwent 40 hours of TCT. We identified target engagement of auditory system processing efficiency by modeling subject-specific trajectories of auditory processing speed (APS) over time. Lowess analysis, mixed models repeated measures analysis, and latent growth curve modeling were used to examine whether APS trajectories were moderated by age and illness duration, and mediated improvements in cognitive outcome measures. We observed significant improvements in APS from baseline to 20 hours of training (initial change), followed by a flat APS trajectory (plateau) at subsequent time-points. Participants showed interindividual variability in the steepness of the initial APS change and in the APS plateau achieved and sustained between 20 and 40 hours. We found that participants who achieved the fastest APS plateau, showed the greatest transfer effects to untrained cognitive domains. There is a significant association between an individual's ability to generate and sustain auditory processing efficiency and their degree of cognitive improvement after TCT, independent of baseline neurocognition. APS plateau may therefore represent a behavioral measure of target engagement mediating treatment response. Future studies should examine the optimal plateau of auditory processing efficiency required to induce significant cognitive improvements, in the context of interindividual differences in neural plasticity and sensory system efficiency that characterize schizophrenia. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  9. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  10. Neural basis for the ability of atypical antipsychotic drugs to improve cognition in schizophrenia

    Directory of Open Access Journals (Sweden)

    Tomiki eSumiyoshi

    2013-10-01

    Full Text Available Cognitive impairments are considered to largely affect functional outcome in patients with schizophrenia, other psychotic illnesses, or mood disorders. Specifically, there is much attention to the role of psychotropic compounds acting on serotonin (5-HT receptors in ameliorating cognitive deficits of schizophrenia.It is noteworthy that atypical antipsychotic drugs, e.g. clozapine, melperone, risperidone, olanzapine, quetiapine, aripiprazole, perospirone, blonanserin, and lurasidone, have variable affinities for these receptors. Among the 5-HT receptor subtypes, the 5-HT1A receptor is attracting particular interests as a potential target for enhancing cognition, based on preclinical and clinical evidence.The neural network underlying the ability of 5-HT1A agonists to treat cognitive impairments of schizophrenia likely includes dopamine, glutamate, and GABA neurons. A novel strategy for cognitive enhancement in psychosis may be benefitted by focusing on energy metabolism in the brain. In this context, lactate plays a major role, and has been shown to protect neurons against oxidative and other stressors. In particular, our data indicate chronic treatment with tandospirone, a partial 5-HT1A agonist, recover stress-induced lactate production in the prefrontal cortex of a rat model of schizophrenia. Recent advances of electrophysiological measures, e.g. event-related potentials, and their imaging have provided insights into facilitative effects on cognition of some atypical antipsychotic drugs acting directly or indirectly on 5-HT1A receptors.These findings are expected to promote the development of novel therapeutics for the improvement of functional outcome in people with schizophrenia.

  11. COGNITIVE SYSTEMS. REDEFINING THE COOPERATION BETWEEN MAN AND SYSTEM

    Directory of Open Access Journals (Sweden)

    Diana-Aderina MOISUC

    2016-12-01

    Full Text Available Cognitive systems appeared as a response to the real challenges brought by the Big Data phenomenon. It was found that the solutions for solving difficult problems caused by this phenomenon could be brought by using artificial intelligence tools. In this context a convergence between Big Data and Artificial Intelligence happened, which determined the start of a new stage in system development, namely the era of cognitive systems. The potential of these systems is given by the characteristics that differentiate them from other systems. The cognitive systems offered solutions and were used with success in complex projects from the medical and financial sectors. The architecture of cognitive systems is complex. These systems are designed so that they use artificial intelligence tools when processing source content, producing analytical solutions which can be used in the decision process. In this paper base concepts, the characteristics and architecture of cognitive systems, the benefits brought by the development and use of them were presented.

  12. Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

    Science.gov (United States)

    Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

    2016-12-01

    How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Neural correlates of naturalistic social cognition: brain-behavior relationships in healthy adults.

    Science.gov (United States)

    Deuse, L; Rademacher, L M; Winkler, L; Schultz, R T; Gründer, G; Lammertz, S E

    2016-11-01

    Being able to infer the thoughts, feelings and intentions of those around us is indispensable in order to function in a social world. Despite growing interest in social cognition and its neural underpinnings, the factors that contribute to successful mental state attribution remain unclear. Current knowledge is limited because the most widely used tasks suffer from two main constraints: (i) They fail to capture individual variability due to ceiling effects and (ii) they use highly simplistic, often artificial stimuli inapt to mirror real-world socio-cognitive demands. In the present study, we address these problems by employing complex depictions of naturalistic social interactions that vary in both valence (positive vs negative) and ambiguity (high vs low). Thirty-eight healthy participants (20 female) made mental state judgments while brain responses were obtained using functional magnetic resonance imaging (fMRI). Accuracy varied based on valence and ambiguity conditions and women were more accurate than men with highly ambiguous social stimuli. Activity of the orbitofrontal cortex predicted performance in the high ambiguity condition. The results shed light on subtle differences in mentalizing abilities and associated neural activity. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

    Science.gov (United States)

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. Neural Correlates of Hostile Jokes: Cognitive and Motivational Processes in Humor Appreciation.

    Science.gov (United States)

    Chan, Yu-Chen; Liao, Yi-Jun; Tu, Cheng-Hao; Chen, Hsueh-Chih

    2016-01-01

    Hostile jokes (HJs) provide aggressive catharsis and a feeling of superiority. Behavioral research has found that HJs are perceived as funnier than non-hostile jokes (NJs). The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing HJs, NJs, and their corresponding hostile sentences (HSs) and non-hostile sentences (NSs). HJs primarily showed activation in the dorsomedial prefrontal cortex (dmPFC) and midbrain compared with the corresponding hostile baseline. Conversely, NJs primarily revealed activation in the ventromedial PFC (vmPFC), amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc) compared with the corresponding non-hostile baseline. These results support the critical role of the medial PFC (mPFC) for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of HJs showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of NJs displayed increased activation in the vmPFC, which suggested social-affective engagement. HJs versus NJs primarily showed increased activation in the dmPFC and midbrain, whereas NJs versus HJs primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI) analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for HJs and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for NJs. Surprisingly, HJs were not perceived as funnier than NJs. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation based on the psychoanalytic and superiority theories of humor.

  16. Neural Correlates of Hostile Jokes: Cognitive and Motivational Processes in Humor Appreciation

    Science.gov (United States)

    Chan, Yu-Chen; Liao, Yi-Jun; Tu, Cheng-Hao

    2016-01-01

    Hostile jokes (HJs) provide aggressive catharsis and a feeling of superiority. Behavioral research has found that HJs are perceived as funnier than non-hostile jokes (NJs). The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing HJs, NJs, and their corresponding hostile sentences (HSs) and non-hostile sentences (NSs). HJs primarily showed activation in the dorsomedial prefrontal cortex (dmPFC) and midbrain compared with the corresponding hostile baseline. Conversely, NJs primarily revealed activation in the ventromedial PFC (vmPFC), amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc) compared with the corresponding non-hostile baseline. These results support the critical role of the medial PFC (mPFC) for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of HJs showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of NJs displayed increased activation in the vmPFC, which suggested social-affective engagement. HJs versus NJs primarily showed increased activation in the dmPFC and midbrain, whereas NJs versus HJs primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI) analysis demonstrated functional coupling of the dmPFC–dlPFC and midbrain–dmPFC for HJs and functional coupling of the vmPFC–midbrain and amygdala–midbrain–NAcc for NJs. Surprisingly, HJs were not perceived as funnier than NJs. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation based on the psychoanalytic and superiority theories of humor. PMID:27840604

  17. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  18. 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.

  19. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  20. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  1. The serotonergic system and cognitive function

    Directory of Open Access Journals (Sweden)

    Švob Štrac Dubravka

    2016-01-01

    Full Text Available Symptoms of cognitive dysfunction like memory loss, poor concentration, impaired learning and executive functions are characteristic features of both schizophrenia and Alzheimer’s disease (AD. The neurobiological mechanisms underlying cognition in healthy subjects and neuropsychiatric patients are not completely understood. Studies have focused on serotonin (5-hydroxytryptamine, 5-HT as one of the possible cognitionrelated biomarkers. The aim of this review is to provide a summary of the current literature on the role of the serotonergic (5-HTergic system in cognitive function, particularly in AD and schizophrenia.

  2. Emotional consciousness: a neural model of how cognitive appraisal and somatic perception interact to produce qualitative experience.

    Science.gov (United States)

    Thagard, Paul; Aubie, Brandon

    2008-09-01

    This paper proposes a theory of how conscious emotional experience is produced by the brain as the result of many interacting brain areas coordinated in working memory. These brain areas integrate perceptions of bodily states of an organism with cognitive appraisals of its current situation. Emotions are neural processes that represent the overall cognitive and somatic state of the organism. Conscious experience arises when neural representations achieve high activation as part of working memory. This theory explains numerous phenomena concerning emotional consciousness, including differentiation, integration, intensity, valence, and change.

  3. Cognitive and neural strategies during control of the anterior cingulate cortex by fMRI neurofeedback in patients with schizophrenia

    Directory of Open Access Journals (Sweden)

    Julia S Cordes

    2015-06-01

    Full Text Available Cognitive functioning is impaired in patients with schizophrenia, leading to significant disabilities in everyday functioning. Its improvement is an important treatment target. Neurofeedback (NF seems a promising method to address the neural dysfunctions underlying those cognitive impairments. The anterior cingulate cortex (ACC, a central hub for cognitive processing, is one of the dysfunctional brain regions in schizophrenia. Here we conducted NF training based on real-time functional magnetic resonance imaging (fMRI in patients with schizophrenia to enable them to control their ACC activity. Training was performed over three days in a group of 11 patients with schizophrenia and 11 healthy controls. Social feedback was provided in accordance with the evoked activity in the selected region of interest (ROI. Neural and cognitive strategies were examined off-line. Both groups learned to control the activity of their ACC but used different neural strategies: Patients activated the dorsal and healthy controls the rostral subdivision. Patients mainly used imagination of music to elicit activity and the control group imagination of sports. However, the difference in neural control did not result from the differences in cognitive strategies but from diagnosis alone. Based on social reinforcers, schizophrenia patients can learn to regulate localized brain activity. Cognitive strategies and neural network location differ, however, from healthy controls. These data emphasize that for therapeutic interventions in schizophrenia compensatory strategies may emerge. Specific cognitive skills or specific dysfunctional networks should be addressed to train impaired skills. Social neurofeedback based on fMRI may be one method to accomplish precise learning targets.

  4. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  5. Different neural and cognitive response to emotional faces in healthy monozygotic twins at risk of depression.

    Science.gov (United States)

    Miskowiak, K W; Glerup, L; Vestbo, C; Harmer, C J; Reinecke, A; Macoveanu, J; Siebner, H R; Kessing, L V; Vinberg, M

    2015-05-01

    Negative cognitive bias and aberrant neural processing of emotional faces are trait-marks of depression. Yet it is unclear whether these changes constitute an endophenotype for depression and are also present in healthy individuals with hereditary risk for depression. Thirty healthy, never-depressed monozygotic (MZ) twins with a co-twin history of depression (high risk group: n = 13) or without co-twin history of depression (low-risk group: n = 17) were enrolled in a functional magnetic resonance imaging (fMRI) study. During fMRI, participants viewed fearful and happy faces while performing a gender discrimination task. After the scan, they were given a faces dot-probe task, a facial expression recognition task and questionnaires assessing mood, personality traits and coping strategies. High-risk twins showed increased neural response to happy and fearful faces in dorsal anterior cingulate cortex (ACC), dorsomedial prefrontal cortex (dmPFC), pre-supplementary motor area and occipito-parietal regions compared to low-risk twins. They also displayed stronger negative coupling between amygdala and pregenual ACC, dmPFC and temporo-parietal regions during emotional face processing. These task-related changes in neural responses in high-risk twins were accompanied by impaired gender discrimination performance during face processing. They also displayed increased attention vigilance for fearful faces and were slower at recognizing facial expressions relative to low-risk controls. These effects occurred in the absence of differences between groups in mood, subjective state or coping. Different neural response and functional connectivity within fronto-limbic and occipito-parietal regions during emotional face processing and enhanced fear vigilance may be key endophenotypes for depression.

  6. Are there pre-existing neural, cognitive, or motoric markers for musical ability?

    Science.gov (United States)

    Norton, Andrea; Winner, Ellen; Cronin, Karl; Overy, Katie; Lee, Dennis J; Schlaug, Gottfried

    2005-11-01

    Adult musician's brains show structural enlargements, but it is not known whether these are inborn or a consequence of long-term training. In addition, music training in childhood has been shown to have positive effects on visual-spatial and verbal outcomes. However, it is not known whether pre-existing advantages in these skills are found in children who choose to study a musical instrument nor is it known whether there are pre-existing associations between music and any of these outcome measures that could help explain the training effects. To answer these questions, we compared 5- to 7-year-olds beginning piano or string lessons (n=39) with 5- to 7-year-olds not beginning instrumental training (n=31). All children received a series of tests (visual-spatial, non-verbal reasoning, verbal, motor, and musical) and underwent magnetic resonance imaging. We found no pre-existing neural, cognitive, motor, or musical differences between groups and no correlations (after correction for multiple analyses) between music perceptual skills and any brain or visual-spatial measures. However, correlations were found between music perceptual skills and both non-verbal reasoning and phonemic awareness. Such pre-existing correlations suggest similarities in auditory and visual pattern recognition as well a sharing of the neural substrates for language and music processing, most likely due to innate abilities or implicit learning during early development. This baseline study lays the groundwork for an ongoing longitudinal study addressing the effects of intensive musical training on brain and cognitive development, and making it possible to look retroactively at the brain and cognitive development of those children who emerge showing exceptional musical talent.

  7. Patterns of longitudinal neural activity linked to different cognitive profiles in Parkinson’s disease

    Directory of Open Access Journals (Sweden)

    Atsuko Nagano-Saito

    2016-11-01

    Full Text Available Mild cognitive impairment in Parkinson’s disease (PD has been linked with functional brain changes. Previously, using functional magnetic resonance imaging (fMRI, we reported reduced cortico-striatal activity in patients with PD who also had mild cognitive impairment (MCI versus those who did not (non-MCI. We followed up these patients to investigate the longitudinal effect on the neural activity. Twenty-four non-demented patients with Parkinson’s disease (non-MCI: 12, MCI; 12 were included in the study. Each participant underwent two fMRIs while performing the Wisconsin Card Sorting Task 20 months apart. The non-MCI patients recruited the usual cognitive corticostriatal loop at the first and second sessions (Time 1 and Time 2, respectively. However, decreased activity was observed in the cerebellum and occipital area and increased activity was observed in the medial prefrontal cortex and parietal lobe during planning set-shift at Time 2. Increased activity in the precuneus was also demonstrated while executing set-shifts at Time 2. The MCI patients revealed more activity in the frontal, parietal and occipital lobes during planning set-shifts, and in the parietal and occipital lobes, precuneus, and cerebellum, during executing set-shift at Time 2. Analysis regrouping of both groups of PD patients revealed that hippocampal and thalamic activity at Time 1 was associated with less cognitive decline over time. Our results reveal that functional alteration along the time-points differed between the non-MCI and MCI patients. They also underline the importance of preserving thalamic and hippocampal function with respect to cognitive decline over time.

  8. Identification of Common Neural Circuit Disruptions in Cognitive Control Across Psychiatric Disorders.

    Science.gov (United States)

    McTeague, Lisa M; Huemer, Julia; Carreon, David M; Jiang, Ying; Eickhoff, Simon B; Etkin, Amit

    2017-07-01

    Cognitive deficits are a common feature of psychiatric disorders. The authors investigated the nature of disruptions in neural circuitry underlying cognitive control capacities across psychiatric disorders through a transdiagnostic neuroimaging meta-analysis. A PubMed search was conducted for whole-brain functional neuroimaging articles published through June 2015 that compared activation in patients with axis I disorders and matched healthy control participants during cognitive control tasks. Tasks that probed performance or conflict monitoring, response inhibition or selection, set shifting, verbal fluency, and recognition or working memory were included. Activation likelihood estimation meta-analyses were conducted on peak voxel coordinates. The 283 experiments submitted to meta-analysis included 5,728 control participants and 5,493 patients with various disorders (schizophrenia, bipolar or unipolar depression, anxiety disorders, and substance use disorders). Transdiagnostically abnormal activation was evident in the left prefrontal cortex as well as the anterior insula, the right ventrolateral prefrontal cortex, the right intraparietal sulcus, and the midcingulate/presupplementary motor area. Disruption was also observed in a more anterior cluster in the dorsal cingulate cortex, which overlapped with a network of structural perturbation that the authors previously reported in a transdiagnostic meta-analysis of gray matter volume. These findings demonstrate a common pattern of disruption across major psychiatric disorders that parallels the "multiple-demand network" observed in intact cognition. This network interfaces with the anterior-cingulo-insular or "salience network" demonstrated to be transdiagnostically vulnerable to gray matter reduction. Thus, networks intrinsic to adaptive, flexible cognition are vulnerable to broad-spectrum psychopathology. Dysfunction in these networks may reflect an intermediate transdiagnostic phenotype, which could be leveraged

  9. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  10. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  11. Neuromodulation and developmental contextual influences on neural and cognitive plasticity across the lifespan.

    Science.gov (United States)

    Li, Shu-Chen

    2013-11-01

    Behavioral, cognitive, and motivational development entails co-constructive interactions between the environmental and social influences from the developmental context, on the one hand, and the individual's neurobiological inheritance, on the other hand. Key brain networks underlying cognition, emotion, and motivation are innervated by major transmitter systems (e.g., the catecholamines and acetylcholine). Thus, the maturation and senescence of neurotransmitter systems have direct implications for lifespan development. In addition to reviewing evidence on life age differences in dopaminergic modulation and cognitive development, this brief review selectively highlights recent findings on how important influences from the developmental context, such as reward-mediated motivational processes, transgenerational stress transmission, psychosocial stress, and cognitive interventions, may, in part, exert their effects on brain and behavioral development through their effects on neuromodulatory mechanisms. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Neural Temporal Dynamics of Facial Emotion Processing: Age Effects and Relationship to Cognitive Function

    Directory of Open Access Journals (Sweden)

    Xiaoyan Liao

    2017-06-01

    Full Text Available This study used event-related potentials (ERPs to investigate the effects of age on neural temporal dynamics of processing task-relevant facial expressions and their relationship to cognitive functions. Negative (sad, afraid, angry, and disgusted, positive (happy, and neutral faces were presented to 30 older and 31 young participants who performed a facial emotion categorization task. Behavioral and ERP indices of facial emotion processing were analyzed. An enhanced N170 for negative faces, in addition to intact right-hemispheric N170 for positive faces, was observed in older adults relative to their younger counterparts. Moreover, older adults demonstrated an attenuated within-group N170 laterality effect for neutral faces, while younger adults showed the opposite pattern. Furthermore, older adults exhibited sustained temporo-occipital negativity deflection over the time range of 200–500 ms post-stimulus, while young adults showed posterior positivity and subsequent emotion-specific frontal negativity deflections. In older adults, decreased accuracy for labeling negative faces was positively correlated with Montreal Cognitive Assessment Scores, and accuracy for labeling neutral faces was negatively correlated with age. These findings suggest that older people may exert more effort in structural encoding for negative faces and there are different response patterns for the categorization of different facial emotions. Cognitive functioning may be related to facial emotion categorization deficits observed in older adults. This may not be attributable to positivity effects: it may represent a selective deficit for the processing of negative facial expressions in older adults.

  13. General emotion processing in social anxiety disorder: neural issues of cognitive control.

    Science.gov (United States)

    Brühl, Annette Beatrix; Herwig, Uwe; Delsignore, Aba; Jäncke, Lutz; Rufer, Michael

    2013-05-30

    Anxiety disorders are characterized by deficient emotion regulation prior to and in anxiety-evoking situations. Patients with social anxiety disorder (SAD) have increased brain activation also during the anticipation and perception of non-specific emotional stimuli pointing to biased general emotion processing. In the current study we addressed the neural correlates of emotion regulation by cognitive control during the anticipation and perception of non-specific emotional stimuli in patients with SAD. Thirty-two patients with SAD underwent functional magnetic resonance imaging during the announced anticipation and perception of emotional stimuli. Half of them were trained and instructed to apply reality-checking as a control strategy, the others anticipated and perceived the stimuli. Reality checking significantly (pperception of negative emotional stimuli. The medial prefrontal cortex was comparably active in both groups (p>0.50). The results suggest that cognitive control in patients with SAD influences emotion processing structures, supporting the usefulness of emotion regulation training in the psychotherapy of SAD. In contrast to studies in healthy subjects, cognitive control was not associated with increased activation of prefrontal regions in SAD. This points to possibly disturbed general emotion regulating circuits in SAD. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

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

  15. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  16. Hybrid cognitive engine for radio systems adaptation

    KAUST Repository

    Alqerm, Ismail

    2017-07-20

    Network efficiency and proper utilization of its resources are essential requirements to operate wireless networks in an optimal fashion. Cognitive radio aims to fulfill these requirements by exploiting artificial intelligence techniques to create an entity called cognitive engine. Cognitive engine exploits awareness about the surrounding radio environment to optimize the use of radio resources and adapt relevant transmission parameters. In this paper, we propose a hybrid cognitive engine that employs Case Based Reasoning (CBR) and Decision Trees (DTs) to perform radio adaptation in multi-carriers wireless networks. The engine complexity is reduced by employing DTs to improve the indexing methodology used in CBR cases retrieval. The performance of our hybrid engine is validated using software defined radios implementation and simulation in multi-carrier environment. The system throughput, signal to noise and interference ratio, and packet error rate are obtained and compared with other schemes in different scenarios.

  17. Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar.

    Science.gov (United States)

    Lomp, Oliver; Richter, Mathis; Zibner, Stephan K U; Schöner, Gregor

    2016-01-01

    Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar , which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

  18. Developing dynamic field theory architectures for embodied cognitive systems with cedar

    Directory of Open Access Journals (Sweden)

    Oliver Lomp

    2016-11-01

    Full Text Available Embodied artificial cognitive systems such as autonomous robots or intelligent observers connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT, a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real-time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

  19. Investigating surety methodologies for cognitive systems.

    Energy Technology Data Exchange (ETDEWEB)

    Caudell, Thomas P. (University of New Mexico, Albuquerque, NM); Peercy, David Eugene; Mills, Kristy (University of New Mexico, Albuquerque, NM); Caldera, Eva (University of New Mexico, Albuquerque, NM)

    2006-11-01

    Advances in cognitive science provide a foundation for new tools that promise to advance human capabilities with significant positive impacts. As with any new technology breakthrough, associated technical and non-technical risks are involved. Sandia has mitigated both technical and non-technical risks by applying advanced surety methodologies in such areas as nuclear weapons, nuclear reactor safety, nuclear materials transport, and energy systems. In order to apply surety to the development of cognitive systems, we must understand the concepts and principles that characterize the certainty of a system's operation as well as the risk areas of cognitive sciences. This SAND report documents a preliminary spectrum of risks involved with cognitive sciences, and identifies some surety methodologies that can be applied to potentially mitigate such risks. Some potential areas for further study are recommended. In particular, a recommendation is made to develop a cognitive systems epistemology framework for more detailed study of these risk areas and applications of surety methods and techniques.

  20. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. [Cognitive function in patients with systemic sclerosis].

    Science.gov (United States)

    Straszecka, J; Jonderko, G; Kucharz, E J; Brzezińska-Wcisło, L; Kotulska, A; Bogdanowski, T

    1997-09-01

    Central nervous system involvement is seldom reported in patients with systemic sclerosis (SSc). Cognitive functions were determined in 21 patients with definite SSc and 42 healthy controls. Thyroid function was also measured in order to eliminate the effect of hypothyroidism on cognitive functioning. It was found that the SSc patients with normal thyroid function showed defective long-term and recent memory, learning ability, criticism, perception and visuo-perceptual skills, their simple reaction time was prolonged. Similar but less advanced cognitive defects were shown in the SSc patients with overt or latent hypothyroidism. The obtained results indicate that the central nervous system involvement is more common in patients with SSc than it has been reported earlier.

  2. On the inference of agency in operant action : an examination of the cognitive and neural underpinnings in health and schizophrenia

    NARCIS (Netherlands)

    Renes, R.A.

    2016-01-01

    This dissertation elucidates cognitive and neural underpinnings of the sense of agency, which is the feeling that we are in control of our actions and the subsequent consequences. This consciously accessible sensation of control is pervasive, sometimes subtle, and can even be illusory in nature.

  3. Neural substrates of cognitive control under the belief of getting neurofeedback training

    Directory of Open Access Journals (Sweden)

    Manuel eNinaus

    2013-12-01

    Full Text Available Learning to modulate one’s own brain activity is the fundament of neurofeedback (NF applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal area were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented.

  4. Neural predictors and mechanisms of cognitive behavioral therapy on threat processing in social anxiety disorder.

    Science.gov (United States)

    Klumpp, Heide; Fitzgerald, Daniel A; Phan, K Luan

    2013-08-01

    Cognitive behavioral therapy (CBT) is "gold standard" psychotherapy for social anxiety disorder (SAD). Cognitive models posit that preferential processing of threat mediates excessive forms of anxiety, which is supported by exaggerated amygdala, insula, and cortical reactivity to threatening socio-emotional signals in SAD. However, little is known about neural predictors of CBT success or the mechanisms by which CBT exerts its therapeutic effects. Functional magnetic resonance imaging (fMRI) was conducted during responses to social signals of threat (fearful/angry faces) against positive signals (happy faces) in 14 patients with SAD before and after 12 weeks of CBT. For comparison, 14 healthy control (HC) participants also underwent two fMRI scans, 12 weeks apart. Whole-brain voxel-wise analyses showed therapeutic success was predicted by enhanced pre-treatment activation to threatening faces in higher-order visual (superior and middle temporal gyrus), cognitive, and emotion processing areas (dorsal anterior cingulate cortex, dorsomedial prefrontal cortex). Moreover, a group by time interaction was revealed in prefrontal regions (dorsomedial, medial gyrus) and insula. The interaction was driven by relatively greater activity during threat processing in SAD, which significantly reduced after CBT but did not significantly predict response to CBT. Therefore, pre-treatment cortical hyperactivity to social threat signals may serve as a prognostic indicator of CBT success in SAD. Collectively, CBT-related brain changes involved a reduction in activity in insula, prefrontal, and extrastriate regions. Results are consistent with cognitive models, which associate decreases in threat processing bias with recovery. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. COGNITIVE SYSTEMS. REDEFINING THE COOPERATION BETWEEN MAN AND SYSTEM

    OpenAIRE

    Diana-Aderina MOISUC; Mihai-Constantin AVORNICULUI; Melinda Timea FÜLÖP

    2016-01-01

    Cognitive systems appeared as a response to the real challenges brought by the Big Data phenomenon. It was found that the solutions for solving difficult problems caused by this phenomenon could be brought by using artificial intelligence tools. In this context a convergence between Big Data and Artificial Intelligence happened, which determined the start of a new stage in system development, namely the era of cognitive systems. The potential of these systems is given by the characteristics t...

  6. Erythropoietin modulates neural and cognitive processing of emotional information in biomarker models of antidepressant drug action in depressed patients

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr

    2010-01-01

    . The current study investigates the effects of Epo on the neural and cognitive response to emotional facial expressions in depressed patients. METHOD: Nineteen acutely depressed patients were randomized to receive Epo (40,000 IU) or saline intravenously in a double-blind, parallel-group design. On day 3, we......OBJECTIVE: Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers...... assessed neuronal responses to fearful and happy faces using functional magnetic resonance imaging and measured facial expression recognition after the scan. RESULTS: Epo reduced neural response to fearful vs. happy faces in the amygdala and hippocampus, and to fearful faces vs. baseline in superior...

  7. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  8. Development of social skills in children: neural and behavioral evidence for the elaboration of cognitive models

    Science.gov (United States)

    Soto-Icaza, Patricia; Aboitiz, Francisco; Billeke, Pablo

    2015-01-01

    Social skills refer to a wide group of abilities that allow us to interact and communicate with others. Children learn how to solve social situations by predicting and understanding other's behaviors. The way in which humans learn to interact successfully with others encompasses a complex interaction between neural, behavioral, and environmental elements. These have a role in the accomplishment of positive developmental outcomes, including peer acceptance, academic achievement, and mental health. All these social abilities depend on widespread brain networks that are recently being studied by neuroscience. In this paper, we will first review the studies on this topic, aiming to clarify the behavioral and neural mechanisms related to the acquisition of social skills during infancy and their appearance in time. Second, we will briefly describe how developmental diseases like Autism Spectrum Disorders (ASD) can inform about the neurobiological mechanisms of social skills. We finally sketch a general framework for the elaboration of cognitive models in order to facilitate the comprehension of human social development. PMID:26483621

  9. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

    Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

    1999-01-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

  10. Energy-Aware Cognitive Radio Systems

    KAUST Repository

    Bedeer, Ebrahim

    2016-01-15

    The concept of energy-aware communications has spurred the interest of the research community in the most recent years due to various environmental and economical reasons. It becomes indispensable for wireless communication systems to shift their resource allocation problems from optimizing traditional metrics, such as throughput and latency, to an environmental-friendly energy metric. Although cognitive radio systems introduce spectrum efficient usage techniques, they employ new complex technologies for spectrum sensing and sharing that consume extra energy to compensate for overhead and feedback costs. Considering an adequate energy efficiency metric—that takes into account the transmit power consumption, circuitry power, and signaling overhead—is of momentous importance such that optimal resource allocations in cognitive radio systems reduce the energy consumption. A literature survey of recent energy-efficient based resource allocations schemes is presented for cognitive radio systems. The energy efficiency performances of these schemes are analyzed and evaluated under power budget, co-channel and adjacent-channel interferences, channel estimation errors, quality-of-service, and/or fairness constraints. Finally, the opportunities and challenges of energy-aware design for cognitive radio systems are discussed.

  11. Cognitive and neural correlates of depression-like behaviour in socially defeated mice: an animal model of depression with cognitive dysfunction.

    Science.gov (United States)

    Yu, Tao; Guo, Ming; Garza, Jacob; Rendon, Samantha; Sun, Xue-Li; Zhang, Wei; Lu, Xin-Yun

    2011-04-01

    Human depression is associated with cognitive deficits. It is critical to have valid animal models in order to investigate mechanisms and treatment strategies for these associated conditions. The goal of this study was to determine the association of cognitive dysfunction with depression-like behaviour in an animal model of depression and investigate the neural circuits underlying the behaviour. Mice that were exposed to social defeat for 14 d developed depression-like behaviour, i.e. anhedonia and social avoidance as indicated by reduced sucrose preference and decreased social interaction. The assessment of cognitive performance of defeated mice demonstrated impaired working memory in the T-maze continuous alternation task and enhanced fear memory in the contextual and cued fear-conditioning tests. In contrast, reference learning and memory in the Morris water maze test were intact in defeated mice. Neuronal activation following chronic social defeat was investigated by c-fosin-situ hybridization. Defeated mice exhibited preferential neural activity in the prefrontal cortex, cingulate cortex, hippocampal formation, septum, amygdala, and hypothalamic nuclei. Taken together, our results suggest that the chronic social defeat mouse model could serve as a valid animal model to study depression with cognitive impairments. The patterns of neuronal activation provide a neural basis for social defeat-induced changes in behaviour.

  12. Supervised cognitive system: A new vision for cognitive engine design in wireless networks

    KAUST Repository

    Alqerm, Ismail; Shihada, Basem

    2018-01-01

    Cognitive radio attracts researchers' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system

  13. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  14. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  15. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  16. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  17. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  18. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  19. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  20. Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

    Science.gov (United States)

    Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

    2017-05-01

    Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

  1. Neural correlation of successful cognitive behaviour therapy for spider phobia: a magnetoencephalography study.

    Science.gov (United States)

    Wright, Barry; Alderson-Day, Ben; Prendergast, Garreth; Kennedy, Juliette; Bennett, Sophie; Docherty, Mary; Whitton, Clare; Manea, Laura; Gouws, Andre; Tomlinson, Heather; Green, Gary

    2013-12-30

    Cognitive behavioural therapy (CBT) can be an effective treatment for spider phobia, but the underlying neural correlates of therapeutic change are yet to be specified. The present study used magnetoencephalography (MEG) to study responses within the first half second, to phobogenic stimuli in a group of individuals with spider phobia prior to treatment (n=12) and then in nine of them following successful CBT (where they could touch and manage live large common house spiders) at least 9 months later. We also compared responses to a group of age-matched healthy control participants (n=11). Participants viewed static photographs of real spiders, other fear-inducing images (e.g. snakes, sharks) and neutral stimuli (e.g. kittens). Beamforming methods were used to localise sources of significant power changes in response to stimuli. Prior to treatment, participants with spider phobia showed a significant maximum response in the right frontal pole when viewing images of real spiders specifically. No significant frontal response was observed for either control participants or participants with spider phobia post-treatment. In addition, participants' subjective ratings of spider stimuli significantly predicted peak responses in right frontal regions. The implications for understanding brain-based effects of cognitive therapies are discussed. © 2013 Published by Elsevier Ireland Ltd.

  2. Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

    Science.gov (United States)

    Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

    2017-03-01

    An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. The relation of ongoing brain activity, evoked neural responses, and cognition

    Directory of Open Access Journals (Sweden)

    Sepideh Sadaghiani

    2010-06-01

    Full Text Available Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a ‘segregationist’ view on ongoing activity, both in time and space, countering this view with an emphasis on integration within a hierarchical spatiotemporal organization of intrinsic activity. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function - provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behaviour.

  4. Cognitive and Neural Effects of Vision-Based Speed-of-Processing Training in Older Adults with Amnestic Mild Cognitive Impairment: A Pilot Study.

    Science.gov (United States)

    Lin, Feng; Heffner, Kathi L; Ren, Ping; Tivarus, Madalina E; Brasch, Judith; Chen, Ding-Geng; Mapstone, Mark; Porsteinsson, Anton P; Tadin, Duje

    2016-06-01

    To examine the cognitive and neural effects of vision-based speed-of-processing (VSOP) training in older adults with amnestic mild cognitive impairment (aMCI) and contrast those effects with an active control (mental leisure activities (MLA)). Randomized single-blind controlled pilot trial. Academic medical center. Individuals with aMCI (N = 21). Six-week computerized VSOP training. Multiple cognitive processing measures, instrumental activities of daily living (IADLs), and two resting state neural networks regulating cognitive processing: central executive network (CEN) and default mode network (DMN). VSOP training led to significantly greater improvements in trained (processing speed and attention: F1,19  = 6.61, partial η(2)  = 0.26, P = .02) and untrained (working memory: F1,19  = 7.33, partial η(2)  = 0.28, P = .01; IADLs: F1,19  = 5.16, partial η(2)  = 0.21, P = .03) cognitive domains than MLA and protective maintenance in DMN (F1, 9  = 14.63, partial η(2)  = 0.62, P = .004). VSOP training, but not MLA, resulted in a significant improvement in CEN connectivity (Z = -2.37, P = .02). Target and transfer effects of VSOP training were identified, and links between VSOP training and two neural networks associated with aMCI were found. These findings highlight the potential of VSOP training to slow cognitive decline in individuals with aMCI. Further delineation of mechanisms underlying VSOP-induced plasticity is necessary to understand in which populations and under what conditions such training may be most effective. © 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.

  5. Cognitive Technologies: The Design of Joint Human-Machine Cognitive Systems

    OpenAIRE

    Woods, David D.

    1985-01-01

    This article explores the implications of one type of cognitive technology, techniques and concepts to develop joint human-machine cognitive systems, for the application of computational technology by examining the joint cognitive system implicit in a hypothetical computer consultant that outputs some form of problem solution. This analysis reveals some of the problems can occur in cognitive system design-e.g., machine control of the interaction, the danger of a responsibility-authority doubl...

  6. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  7. Cognitive Systems Engineering: The Next 30 Years

    Science.gov (United States)

    Feary, Michael

    2012-01-01

    This presentation is part of panel discussion on Cognitive Systems Engineering. The purpose of this panel is to discuss the challenges and future directions of Cognitive Systems Engineering for the next 30 years. I intended to present the work we have been doing with the Aviation Safety program and Space Human Factors Engineering project on Work Domain Analysis and some areas of Research Focus. Specifically, I intend to focus on the shift on the need to understand and model attention in mixed-initiative systems, the need for methods which can generate results to be used in trade-off decisions, and the need to account for a range of human behavior in the design.

  8. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  9. Early Cognitive Vision as a Frontend for Cognitive Systems

    DEFF Research Database (Denmark)

    Krüger, Norbert; Pugeault, Nicolas; Baseski, Emre

    We discuss the need of an elaborated in-between stage bridging early vision and cognitive vision which we call `Early Cognitive Vision' (ECV). This stage provides semantically rich, disambiguated and largely task independent scene representations which can be used in many contexts. In addition...

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

    Science.gov (United States)

    Uluyol, Onder

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

  11. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  12. 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.

  13. A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment.

    Science.gov (United States)

    Wen, Dong; Xue, Qing; Lu, Chengbiao; Guan, Xinyong; Wang, Yuping; Li, Xiaoli

    2014-08-01

    Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings. Copyright © 2014. Published by Elsevier Ltd.

  14. The neural substrates of cognitive flexibility are related to individual differences in preschool irritability: A fNIRS investigation

    Directory of Open Access Journals (Sweden)

    Yanwei Li

    2017-06-01

    Full Text Available Preschool (age 3–5 is a phase of rapid development in both cognition and emotion, making this a period in which the neurodevelopment of each domain is particularly sensitive to that of the other. During this period, children rapidly learn how to flexibly shift their attention between competing demands and, at the same time, acquire critical emotion regulation skills to respond to negative affective challenges. The integration of cognitive flexibility and individual differences in irritability may be an important developmental process of early childhood maturation. However, at present it is unclear if they share common neural substrates in early childhood. Our main goal was to examine the neural correlates of cognitive flexibility in preschool children and test for associations with irritability. Forty-six preschool aged children completed a novel, child-appropriate, Stroop task while dorsolateral prefrontal cortex (DLPFC activation was recorded using functional Near Infrared Spectroscopy (fNIRS. Parents rated their child’s irritability. Results indicated that left DLPFC activation was associated with cognitive flexibility and positively correlated with irritability. Right DLPFC activation was also positively correlated with irritability. Results suggest the entwined nature of cognitive and emotional neurodevelopment during a developmental period of rapid and mutual acceleration.

  15. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  16. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  17. 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 ...

  18. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

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

    Science.gov (United States)

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

    2015-04-01

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

  20. The Effect of an Enrichment Reading Program on the Cognitive Processes and Neural Structures of Children Having Reading Difficulties

    Directory of Open Access Journals (Sweden)

    Hayriye Gül KURUYER

    2017-06-01

    Full Text Available The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This research focuses on a group of eight students with reading difficulties. Within the context of the study, memory capacities, attention spans, reading-related activation and white matter pathways of the students were determined before and after the application of the enrichment reading program. This determination process was carried out in two stages. Neuro-imaging was performed in the first stage and in the second stage the students’ cognitive processes and neural structures were investigated in terms of focusing attention and memory capacities by using the following tools: Stroop Test TBAG Form, Auditory Verbal Digit Span Test-Form B, Cancellation Test and Number Order Learning Test. The results obtained show that the enrichment reading program resulted in an improvement in the reading profiles of the students having reading difficulties in terms of their cognitive processes and neural structures.

  1. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  2. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  3. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  4. Neural basis of attachment-caregiving systems interaction:insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

    Full Text Available The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the attachment-caregiving social bond (Bowlby, 1969. We then follow with those neuroimaging studies that have focused on this particular issue, i.e. those which have studied the activation of the careging system in women (using infant stimuli and have explored how the individual attachment model (through the Adult Attachment Interview modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction.

  5. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  7. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  8. Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network.

    Science.gov (United States)

    Wessing, Ida; Rehbein, Maimu A; Romer, Georg; Achtergarde, Sandra; Dobel, Christian; Zwitserlood, Pienie; Fürniss, Tilman; Junghöfer, Markus

    2015-06-01

    Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP) can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8-14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. A Layered Active Memory Architecture for Cognitive Vision Systems

    OpenAIRE

    Kolonias, Ilias; Christmas, William; Kittler, Josef

    2007-01-01

    Recognising actions and objects from video material has attracted growing research attention and given rise to important applications. However, injecting cognitive capabilities into computer vision systems requires an architecture more elaborate than the traditional signal processing paradigm for information processing. Inspired by biological cognitive systems, we present a memory architecture enabling cognitive processes (such as selecting the processes required for scene understanding, laye...

  10. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  11. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  12. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  13. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  14. Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

    Science.gov (United States)

    Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

    2017-10-01

    All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

  15. Polysaccharides from Ganoderma lucidum Promote Cognitive Function and Neural Progenitor Proliferation in Mouse Model of Alzheimer's Disease.

    Science.gov (United States)

    Huang, Shichao; Mao, Jianxin; Ding, Kan; Zhou, Yue; Zeng, Xianglu; Yang, Wenjuan; Wang, Peipei; Zhao, Cun; Yao, Jian; Xia, Peng; Pei, Gang

    2017-01-10

    Promoting neurogenesis is a promising strategy for the treatment of cognition impairment associated with Alzheimer's disease (AD). Ganoderma lucidum is a revered medicinal mushroom for health-promoting benefits in the Orient. Here, we found that oral administration of the polysaccharides and water extract from G. lucidum promoted neural progenitor cell (NPC) proliferation to enhance neurogenesis and alleviated cognitive deficits in transgenic AD mice. G. lucidum polysaccharides (GLP) also promoted self-renewal of NPC in cell culture. Further mechanistic study revealed that GLP potentiated activation of fibroblast growth factor receptor 1 (FGFR1) and downstream extracellular signal-regulated kinase (ERK) and AKT cascades. Consistently, inhibition of FGFR1 effectively blocked the GLP-promoted NPC proliferation and activation of the downstream cascades. Our findings suggest that GLP could serve as a regenerative therapeutic agent for the treatment of cognitive decline associated with neurodegenerative diseases. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Music for the ageing brain: Cognitive, emotional, social, and neural benefits of musical leisure activities in stroke and dementia.

    Science.gov (United States)

    Särkämö, Teppo

    2017-01-01

    Music engages an extensive network of auditory, cognitive, motor, and emotional processing regions in the brain. Coupled with the fact that the emotional and cognitive impact of music is often well preserved in ageing and dementia, music is a powerful tool in the care and rehabilitation of many ageing-related neurological diseases. In addition to formal music therapy, there has been a growing interest in self- or caregiver-implemented musical leisure activities or hobbies as a widely applicable means to support psychological wellbeing in ageing and in neurological rehabilitation. This article reviews the currently existing evidence on the cognitive, emotional, and neural benefits of musical leisure activities in normal ageing as well as in the rehabilitation and care of two of the most common and ageing-related neurological diseases: stroke and dementia.

  17. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  18. Different Neural Systems Contribute to Semantic Bias and Conflict Detection in the Inclusion Fallacy Task

    Directory of Open Access Journals (Sweden)

    Peipeng eLiang

    2014-10-01

    Full Text Available more general conclusion category is considered stronger than a generalization to a specific conclusion category nested within the more general set. Such inferences violate rational norms and are part of the reasoning fallacy literature that provides interesting tasks to explore cognitive and neural basis of reasoning. To explore the functional neuroanatomy of the inclusion fallacy, we used a 2×2 factorial design, with factors for Quantification (explicit and implicit and Response (fallacious and nonfallacious. It was found that a left fronto-temporal system, along with a superior medial frontal system, was specifically activated in response to fallacy responses consistent with a semantic biasing of judgment explanation. A right fronto-parietal system was specifically recruited in response to detecting conflict associated with the heightened fallacy condition. These results are largely consistent with previous studies of reasoning fallacy and support a multiple systems model of reasoning.

  19. A Game System for Cognitive Rehabilitation

    Directory of Open Access Journals (Sweden)

    Azrulhizam Shapi’i

    2015-01-01

    Full Text Available Brain injury such as traumatic brain injury (TBI and stroke is the major cause of long-term disabilities in many countries. The increasing rate of brain damaged victims and the heterogeneity of impairments decrease rehabilitation effectiveness and competence resulting in higher cost of rehabilitation treatment. On the other hand, traditional rehabilitation exercises are boring, thus leading patients to neglect the prescribed exercises required for recovery. Therefore, we propose game-based approach to address these problems. This paper presents a rehabilitation gaming system (RGS for cognitive rehabilitation. The RGS is developed based on a proposed conceptual framework which has also been presented in this paper.

  20. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  1. Proactive learning for artificial cognitive systems

    Science.gov (United States)

    Lee, Soo-Young

    2010-04-01

    The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

  2. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  3. An Equal Start: Absence of Group Differences in Cognitive, Social and Neural Measures Prior to Music or Sports Training in Children.

    Directory of Open Access Journals (Sweden)

    Assal eHabibi

    2014-09-01

    Full Text Available Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6-7 year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.

  4. An equal start: absence of group differences in cognitive, social, and neural measures prior to music or sports training in children.

    Science.gov (United States)

    Habibi, Assal; Ilari, Beatriz; Crimi, Kevin; Metke, Michael; Kaplan, Jonas T; Joshi, Anand A; Leahy, Richard M; Shattuck, David W; Choi, So Y; Haldar, Justin P; Ficek, Bronte; Damasio, Antonio; Damasio, Hanna

    2014-01-01

    Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long-term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6- to 7-year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional, and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional, and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional, or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.

  5. Disassembly automation automated systems with cognitive abilities

    CERN Document Server

    Vongbunyong, Supachai

    2015-01-01

    This book presents a number of aspects to be considered in the development of disassembly automation, including the mechanical system, vision system and intelligent planner. The implementation of cognitive robotics increases the flexibility and degree of autonomy of the disassembly system. Disassembly, as a step in the treatment of end-of-life products, can allow the recovery of embodied value left within disposed products, as well as the appropriate separation of potentially-hazardous components. In the end-of-life treatment industry, disassembly has largely been limited to manual labor, which is expensive in developed countries. Automation is one possible solution for economic feasibility. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.

  6. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  7. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  8. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  9. Music perception and cognition: development, neural basis, and rehabilitative use of music.

    Science.gov (United States)

    Särkämö, Teppo; Tervaniemi, Mari; Huotilainen, Minna

    2013-07-01

    Music is a highly versatile form of art and communication that has been an essential part of human society since its early days. Neuroimaging studies indicate that music is a powerful stimulus also for the human brain, engaging not just the auditory cortex but also a vast, bilateral network of temporal, frontal, parietal, cerebellar, and limbic brain areas that govern auditory perception, syntactic and semantic processing, attention and memory, emotion and mood control, and motor skills. Studies of amusia, a severe form of musical impairment, highlight the right temporal and frontal cortices as the core neural substrates for adequate perception and production of music. Many of the basic auditory and musical skills, such as pitch and timbre perception, start developing already in utero, and babies are born with a natural preference for music and singing. Music has many important roles and functions throughout life, ranging from emotional self-regulation, mood enhancement, and identity formation to promoting the development of verbal, motor, cognitive, and social skills and maintaining their healthy functioning in old age. Music is also used clinically as a part of treatment in many illnesses, which involve affective, attention, memory, communication, or motor deficits. Although more research is still needed, current evidence suggests that music-based rehabilitation can be effective in many developmental, psychiatric, and neurological disorders, such as autism, depression, schizophrenia, and stroke, as well as in many chronic somatic illnesses that cause pain and anxiety. WIREs Cogn Sci 2013, 4:441-451. doi: 10.1002/wcs.1237 The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. Copyright © 2013 John Wiley & Sons, Ltd.

  10. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  11. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

  12. Classification of cognitive systems dedicated to data sharing

    Science.gov (United States)

    Ogiela, Lidia; Ogiela, Marek R.

    2017-08-01

    In this paper will be presented classification of new cognitive information systems dedicated to cryptographic data splitting and sharing processes. Cognitive processes of semantic data analysis and interpretation, will be used to describe new classes of intelligent information and vision systems. In addition, cryptographic data splitting algorithms and cryptographic threshold schemes will be used to improve processes of secure and efficient information management with application of such cognitive systems. The utility of the proposed cognitive sharing procedures and distributed data sharing algorithms will be also presented. A few possible application of cognitive approaches for visual information management and encryption will be also described.

  13. Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging.

    Science.gov (United States)

    Bambini, Valentina; Gentili, Claudio; Ricciardi, Emiliano; Bertinetto, Pier Marco; Pietrini, Pietro

    2011-10-10

    Prior neuroimaging studies on metaphor comprehension have tended to focus on the role of the right hemisphere, without reaching consensus and leaving aside the functional architecture of this process. The present work aimed to break down metaphor comprehension into its functional components. The study rationale is two-fold: on the one hand, the large-scale network model as emerging in cognitive neuroscience led us to a consideration of metaphor as supported by a distributed and bilateral network; on the other hand, we based on the accounts of figurative language put forward in pragmatics and cognitive science to postulate a decomposition of such a network into multiple sub-systems. During scanning, participants implicitly processed metaphorical (familiar and unfamiliar) and non-metaphorical passages, while being explicitly involved in an adjective matching task to be performed after reading the target passages. Several regions showed greater activity to metaphors as compared to non-metaphors, including left and right inferior frontal gyrus, right superior temporal gyrus, left angular gyrus, and anterior cingulate. This pattern of activations, markedly bilateral, can be decomposed into circumscribed functional sub-systems mediating different aspects of metaphor resolution, as foreseen in the pragmatic and cognitive literature: (a) the conceptual/pragmatic machinery in the bilateral inferior frontal gyrus and in the left angular gyrus, which supports the integration of linguistic material and world knowledge in context; (b) the attentional component in the anterior cingulate and prefrontal areas, which is set to monitor and filter for the relevant aspects of context and for the appropriate meanings; (c) the Theory of Mind system along the right superior temporal sulcus, which deals with the recognition of speakers' communicative intentions and is more extensively activated by unfamiliar metaphors. The results have several implications for the field of neuropragmatics

  14. Cognitive models embedded in system simulation models

    International Nuclear Information System (INIS)

    Siegel, A.I.; Wolf, J.J.

    1982-01-01

    If we are to discuss and consider cognitive models, we must first come to grips with two questions: (1) What is cognition; (2) What is a model. Presumably, the answers to these questions can provide a basis for defining a cognitive model. Accordingly, this paper first places these two questions into perspective. Then, cognitive models are set within the context of computer simulation models and a number of computer simulations of cognitive processes are described. Finally, pervasive issues are discussed vis-a-vis cognitive modeling in the computer simulation context

  15. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

    An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

  16. Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems

    Science.gov (United States)

    Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.

    2015-01-01

    According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274

  17. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

  18. Sympathetic neural modulation of the immune system

    International Nuclear Information System (INIS)

    Madden, K.S.

    1989-01-01

    One route by which the central nervous system communicates with lymphoid organs in the periphery is through the sympathetic nervous system (SNS). To study SNS regulation of immune activity in vivo, selective removal of peripheral noradrenergic nerve fibers was achieved by administration of the neurotoxic drug, 6-hydroxydopamine (6-OHDA), to adult mice. To assess SNS influence on lymphocyte proliferation in vitro, uptake of 125 iododeoxyuridine ( 125 IUdR), a DNA precursor, was measured following 6-OHDA treatment. Sympathectomy prior to epicutaneous immunization with TNCB did not alter draining lymph nodes (LN) cell proliferation, whereas 6-OHDA treatment before footpad immunization with KLH reduced DNA synthesis in popliteal LN by 50%. In mice which were not deliberately immunized, sympathectomy stimulated 125 IUdR uptake inguinal and axillary LN, spleen, and bone marrow. In vitro, these LN and spleen cells exhibited decreased proliferation responses to the T cell mitogen, concanavalin A (Con A), whereas lipopolysaccharide (LPS)-stimulated IgG secretion was enhanced. Studies examining 51 Cr-labeled lymphocyte trafficking to LN suggested that altered cell migration may play a part in sympathectomy-induced changes in LN cell function

  19. Cyclosporin A-Mediated Activation of Endogenous Neural Precursor Cells Promotes Cognitive Recovery in a Mouse Model of Stroke

    Directory of Open Access Journals (Sweden)

    Labeeba Nusrat

    2018-04-01

    Full Text Available Cognitive dysfunction following stroke significantly impacts quality of life and functional independance; yet, despite the prevalence and negative impact of cognitive deficits, post-stroke interventions almost exclusively target motor impairments. As a result, current treatment options are limited in their ability to promote post-stroke cognitive recovery. Cyclosporin A (CsA has been previously shown to improve post-stroke functional recovery of sensorimotor deficits. Interestingly, CsA is a commonly used immunosuppressant and also acts directly on endogenous neural precursor cells (NPCs in the neurogenic regions of the brain (the periventricular region and the dentate gyrus. The immunosuppressive and NPC activation effects are mediated by calcineurin-dependent and calcineurin-independent pathways, respectively. To develop a cognitive stroke model, focal bilateral lesions were induced in the medial prefrontal cortex (mPFC of adult mice using endothelin-1. First, we characterized this stroke model in the acute and chronic phase, using problem-solving and memory-based cognitive tests. mPFC stroke resulted in early and persistent deficits in short-term memory, problem-solving and behavioral flexibility, without affecting anxiety. Second, we investigated the effects of acute and chronic CsA treatment on NPC activation, neuroprotection, and tissue damage. Acute CsA administration post-stroke increased the size of the NPC pool. There was no effect on neurodegeneration or lesion volume. Lastly, we looked at the effects of chronic CsA treatment on cognitive recovery. Long-term CsA administration promoted NPC migration toward the lesion site and rescued cognitive deficits to control levels. This study demonstrates that CsA treatment activates the NPC population, promotes migration of NPCs to the site of injury, and leads to improved cognitive recovery following long-term treatment.

  20. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  1. Contribution Towards Practical Cognitive Radios Systems

    KAUST Repository

    Ben Ghorbel, Mahdi

    2013-07-01

    Cognitive radios is one of the hot topics for emerging and future wireless commu- nication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. The objective of our work is to propose some enhancements to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum (dynamic resource allocation). Thus, the work is composed of two main parts. The first part focuses on cooperative spectrum sensing. We compute in this part the analytical performance of cooperative spectrum sensing under non identical and imperfect channels. Different schemes are considered for the cooperation between users such as hard binary, censored information, quantized, and soft information. The second part focuses on the dynamic resource allocation. We first propose low-cost re- source allocation algorithms that use location information to estimate the interference to primary users to replace absence of instantaneous channel state information. We extend these algorithms to handle practical implementation constraints such as dis- 5 crete bit-loading and collocated subcarriers allocations. We then propose a reduced dimension approach based on the grouping of subcarriers into clusters and performing the resource allocation over clusters of subcarriers instead of single subcarriers. This approach is shown to reduce the computational complexity of the algorithm with lim- ited performance loss. In addition, it is valid for a generic set of resource allocation problems in presence of co-channel interference between users.

  2. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  3. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  4. Neural responses to affective and cognitive theory of mind in children and adolescents with autism spectrum disorder.

    Science.gov (United States)

    Kim, Eunjoo; Kyeong, Sunghyon; Cheon, Keun-Ah; Park, Bumhee; Oh, Maeng-Keun; Chun, Ji Won; Park, Hae-Jeong; Kim, Jae-Jin; Song, Dong-Ho

    2016-05-16

    Children and adolescents with Autism Spectrum Disorder (ASD) are characterized by an impaired Theory of Mind (ToM). Recent evidence suggested that two aspects of ToM (cognitive ToM versus affective ToM) are differentially impaired in individuals with ASD. In this study, we examined the neural correlates of cognitive and affective ToM in children and adolescents with ASD compared to typically developing children (TDCs). Twelve children and adolescents with ASD and 12 age, IQ matched TDCs participated in this functional MRI study. The ToM task involved the attribution of cognitive and affective mental states to a cartoon character based on verbal and eye-gaze cues. In cognitive ToM tasks, ASD participants recruited the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and superior temporal gyrus (STG) to a greater extent than did TDCs. In affective ToM tasks, both ASD and TDC participants showed more activation in the insula and other subcortical regions than in cognitive ToM tasks. Correlational analysis revealed that greater activation of the mPFC/ACC regions was associated with less symptom severity in ASD patients. In sum, our study suggests that the recruitment of additional prefrontal resources can compensate for the successful behavioral performance in the ToM task in ASD participants. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system

    International Nuclear Information System (INIS)

    Kheirandish, Azadeh; Motlagh, Farid; Shafiabady, Niusha; Dahari, Mahidzal; Khairi Abdul Wahab, Ahmad

    2017-01-01

    Highlights: •Fuzzy cognitive map was proposed for the first time to describe the behaviour of fuel cell electric bicycle system. •Fuzzy rules were applied to explain the cause and effect between concepts. •To predict and analyse the cognitive map involved in the negotiation process. -- Abstract: Modelling Proton Exchange Membrane Fuel Cell (PEMFC) is the fundamental step in designing efficient systems for achieving higher performance. Among the development of new energy technologies, modelling and optimization of energy processes with pollution reduction, sufficient efficiency and low emission are considered one of the most promising areas of study. Despite affecting factors in PEMFC functionality, providing a reliable model for PEMFC is the key of performance optimization challenge. In this paper, fuzzy cognitive map has been used for modelling PEMFC system that is directed to provide a dynamic cognitive map from the affecting factors of the system. Controlling and modification of the system performance in various conditions is more practical by correlations among the performance factors of the PEMFC derived from fuzzy cognitive maps. On the other hand, the information of fuzzy cognitive map modelling is applicable for modification of neural networks structure for providing more accurate results based on the extracted knowledge from the cognitive map and visualization of the system’s performance. Finally, a rule based fuzzy cognitive map has been used that can be implemented for decision-making to control the system. This rule-based approach provides interpretability while enhancing the performance of the overall system.

  6. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

  7. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  8. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  9. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  10. Semi-empirical neural network models of controlled dynamical systems

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

    Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

  11. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  12. Do subjective cognitive complaints correlate with cognitive impairment in systemic lupus erythematosus? A Danish outpatient study

    DEFF Research Database (Denmark)

    Vogel, A; Bhattacharya, S; Larsen, J L

    2011-01-01

    This study examined the prevalence of cognitive impairment and its association with depressive symptoms and self-reported cognitive complaints in Danish outpatients with systemic lupus erythematosus (SLE). Fifty-seven consecutive female SLE-outpatients were examined with a comprehensive neuropsyc......This study examined the prevalence of cognitive impairment and its association with depressive symptoms and self-reported cognitive complaints in Danish outpatients with systemic lupus erythematosus (SLE). Fifty-seven consecutive female SLE-outpatients were examined with a comprehensive...

  13. The Dose-Dependent Effects of Vascular Risk Factors on Dynamic Compensatory Neural Processes in Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Haifeng Chen

    2018-05-01

    Full Text Available Background/Objectives: Mild cognitive impairment (MCI has been associated with risk for Alzheimer's Disease (AD. Previous investigations have suggested that vascular risk factors (VRFs were associated with cognitive decline and AD pathogenesis, and the intervention of VRFs may be a possible way to prevent dementia. However, in MCI, little is known about the potential impacts of VRFs on neural networks and their neural substrates, which may be a neuroimaging biomarker of the disease progression.Methods: 128 elderly Han Chinese participants (67 MCI subjects and 61 matched normal elderly with or without VRFs (hypertension, diabetes mellitus, hypercholesterolemia, smoking and alcohol drinking underwent the resting-state functional magnetic resonance imaging (fMRI and neuropsychological tests. We obtained the default mode network (DMN to identify alterations in MCI with the varying number of the VRF and analyzed the significant correlation with behavioral performance.Results: The effects of VRF on the DMN were primarily in bilateral dorsolateral prefrontal cortex (DLPFC (i.e., middle frontal gyrus. Normal elderly showed the gradually increased functional activity of DLPFC, while a fluctuant activation of DLPFC was displayed in MCI with the growing number of the VRF. Interestingly, the left DLPFC further displayed significantly dynamic correlation with executive function as the variation of VRF loading. Initial level of compensation was observed in normal aging and none-vascular risk factor (NVRF MCI, while these compensatory neural processes were suppressed in One-VRF MCI and were subsequently re-aroused in Over-One-VRF MCI.Conclusions: These findings suggested that the dose-dependent effects of VRF on DLPFC were highlighted in MCI, and the dynamic compensatory neural processes that fluctuated along with variations of VRF loading could be key role in the progression of MCI.

  14. Fit in the Body: Matching Embodied Cognition with Social-Ecological Systems

    Directory of Open Access Journals (Sweden)

    Janne I. Hukkinen

    2012-12-01

    Full Text Available Analysis of fit has focused on the macrolevel fit between social institutions and ecosystems, and bypassed the microlevel fit between individual cognition and its socio-material environment. I argue that the conceptualizations we develop about social-ecological systems and our position in them should be understood as ways for a fundamentally cognitive organism to adapt to particular social and ecological situations. Since at issue is our survival as a species, we need to better understand the structure and dynamics of fit between human cognition and its social-ecological environment. I suggest that the embodied cognition perspective opens up possibilities for "nudging" evolution through the conceptual integration of the cognitively attractive but ecologically unrealistic neoclassical economics, and the cognitively less attractive but ecologically more realistic adaptive cycle theory (panarchy. The result is a conceptually integrated model, the Roller Coaster Blend, which expresses in metaphorical terms why competitive individuals are better off cooperating than competing with each other in the face of absolute resource limits. The blend enables the reframing of messages about the limits of the social-ecological system in terms of growth rather than degrowth. This is cognitively appealing, as upward growth fires in our minds the neural connections of "more," "control", and "happy." The blend's potential for nudging behavior arises from its autopoietic characteristic: it can be both an account of the social-ecological system as an emergent structure that is capable of renewing itself, and a cognitive attractor of individuals whose recruitment reinforces the integrity of the social-ecological system.

  15. Functional overlap of top-down emotion regulation and generation: an fMRI study identifying common neural substrates between cognitive reappraisal and cognitively generated emotions.

    Science.gov (United States)

    Otto, Benjamin; Misra, Supriya; Prasad, Aditya; McRae, Kateri

    2014-09-01

    One factor that influences the success of emotion regulation is the manner in which the regulated emotion was generated. Recent research has suggested that reappraisal, a top-down emotion regulation strategy, is more effective in decreasing self-reported negative affect when emotions were generated from the top-down, versus the bottom-up. On the basis of a process overlap framework, we hypothesized that the neural regions active during reappraisal would overlap more with emotions that were generated from the top-down, rather than from the bottom-up. In addition, we hypothesized that increased neural overlap between reappraisal and the history effects of top-down emotion generation would be associated with increased reappraisal success. The results of several analyses suggested that reappraisal and emotions that were generated from the top-down share a core network of prefrontal, temporal, and cingulate regions. This overlap is specific; no such overlap was observed between reappraisal and emotions that were generated in a bottom-up fashion. This network consists of regions previously implicated in linguistic processing, cognitive control, and self-relevant appraisals, which are processes thought to be crucial to both reappraisal and top-down emotion generation. Furthermore, individuals with high reappraisal success demonstrated greater neural overlap between reappraisal and the history of top-down emotion generation than did those with low reappraisal success. The overlap of these key regions, reflecting overlapping processes, provides an initial insight into the mechanism by which generation history may facilitate emotion regulation.

  16. Using EEG to Discriminate Cognitive Workload and Performance Based on Neural Activation and Connectivity

    Science.gov (United States)

    2016-05-31

    in memory . Cognitive efficacy is assessed based on accuracy in recalling digits from memory . A Gaussian classifier is used to discriminate cognitive...high) and 2) cognitive performance (correct recall , incorrect recall ). Our paper is organized as follows. In Section 2, the auditory working memory ...data collection is described, which uses a novel cognitive load protocol that taxes auditory working memory by eliciting recall of sentences and

  17. The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers

    Science.gov (United States)

    Botvinick, Matthew M.; Cohen, Jonathan D.

    2014-01-01

    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on…

  18. Neural Signatures of Cognitive Flexibility and Reward Sensitivity Following Nicotinic Receptor Stimulation in Dependent Smokers: A Randomized Trial.

    Science.gov (United States)

    Lesage, Elise; Aronson, Sarah E; Sutherland, Matthew T; Ross, Thomas J; Salmeron, Betty Jo; Stein, Elliot A

    2017-06-01

    Withdrawal from nicotine is an important contributor to smoking relapse. Understanding how reward-based decision making is affected by abstinence and by pharmacotherapies such as nicotine replacement therapy and varenicline tartrate may aid cessation treatment. To independently assess the effects of nicotine dependence and stimulation of the nicotinic acetylcholine receptor on the ability to interpret valence information (reward sensitivity) and subsequently alter behavior as reward contingencies change (cognitive flexibility) in a probabilistic reversal learning task. Nicotine-dependent smokers and nonsmokers completed a probabilistic reversal learning task during acquisition of functional magnetic resonance imaging (fMRI) in a 2-drug, double-blind placebo-controlled crossover design conducted from January 21, 2009, to September 29, 2011. Smokers were abstinent from cigarette smoking for 12 hours for all sessions. In a fully Latin square fashion, participants in both groups underwent MRI twice while receiving varenicline and twice while receiving a placebo pill, wearing either a nicotine or a placebo patch. Imaging analysis was performed from June 15, 2015, to August 10, 2016. A well-established computational model captured effects of smoking status and administration of nicotine and varenicline on probabilistic reversal learning choice behavior. Neural effects of smoking status, nicotine, and varenicline were tested for on MRI contrasts that captured reward sensitivity and cognitive flexibility. The study included 24 nicotine-dependent smokers (12 women and 12 men; mean [SD] age, 35.8 [9.9] years) and 20 nonsmokers (10 women and 10 men; mean [SD] age, 30.4 [7.2] years). Computational modeling indicated that abstinent smokers were biased toward response shifting and that their decisions were less sensitive to the available evidence, suggesting increased impulsivity during withdrawal. These behavioral impairments were mitigated with nicotine and varenicline

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

    CERN Document Server

    Liu, Jinkun

    2013-01-01

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

  20. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  1. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  2. A neural cell adhesion molecule-derived peptide reduces neuropathological signs and cognitive impairment induced by Abeta25-35

    DEFF Research Database (Denmark)

    Klementiev, B; Novikova, T; Novitskaya, V

    2007-01-01

    death and brain atrophy in response to Abeta25-35. Finally, the Abeta25-35-administration led to a reduced short-term memory as determined by the social recognition test. A synthetic peptide termed FGL derived from the neural cell adhesion molecule (NCAM) was able to prevent or, if already manifest......, strongly reduce all investigated signs of Abeta25-35-induced neuropathology and cognitive impairment. The FGL peptide was recently demonstrated to be able to cross the blood-brain-barrier. Accordingly, we found that the beneficial effects of FGL were achieved not only by intracisternal, but also...... and cognitive impairment involves the modulation of intracellular signal-transduction mediated through GSK3beta....

  3. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  4. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  5. Neurodynamics of Cognition and Consciousness

    CERN Document Server

    Perlovsky, Leonid I

    2007-01-01

    This book addresses dynamical aspects of brain functions and cognition. Experimental evidence in humans and other mammalians indicates that complex neurodynamics is crucial for the emergence of higher-level cognition and consciousness. Dynamical neural systems with encoding in limit cycle and non-convergent attractors have gained increasing popularity in the past decade. The role of synchronization, desynchronization, and intermittent synchronization on cognition has been studied extensively by various authors, in particular by authors contributing to the present volume. This volume gives an overview of recent advances in this interdisciplinary field of cognitive and computer science related to dynamics of cognition, including experimental studies, dynamical modelling and interpretation of cognitive experiments, and theoretical approaches. The following topics are covered in this book: spatio-temporal dynamics of neural correlates of higher-level cognition; dynamical neural memories, including continuous and ...

  6. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  7. Olfactory systems and neural circuits that modulate predator odor fear

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  8. Cognitive Systems Modeling and Analysis of Command and Control Systems

    Science.gov (United States)

    Norlander, Arne

    2012-01-01

    Military operations, counter-terrorism operations and emergency response often oblige operators and commanders to operate within distributed organizations and systems for safe and effective mission accomplishment. Tactical commanders and operators frequently encounter violent threats and critical demands on cognitive capacity and reaction time. In the future they will make decisions in situations where operational and system characteristics are highly dynamic and non-linear, i.e. minor events, decisions or actions may have serious and irreversible consequences for the entire mission. Commanders and other decision makers must manage true real time properties at all levels; individual operators, stand-alone technical systems, higher-order integrated human-machine systems and joint operations forces alike. Coping with these conditions in performance assessment, system development and operational testing is a challenge for both practitioners and researchers. This paper reports on research from which the results led to a breakthrough: An integrated approach to information-centered systems analysis to support future command and control systems research development. This approach integrates several areas of research into a coherent framework, Action Control Theory (ACT). It comprises measurement techniques and methodological advances that facilitate a more accurate and deeper understanding of the operational environment, its agents, actors and effectors, generating new and updated models. This in turn generates theoretical advances. Some good examples of successful approaches are found in the research areas of cognitive systems engineering, systems theory, and psychophysiology, and in the fields of dynamic, distributed decision making and naturalistic decision making.

  9. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  10. 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.

  11. Discovering the Neural Nature of Moral Cognition? Empirical, Theoretical, and Practical Challenges in Bioethical Research with Electroencephalography (EEG).

    Science.gov (United States)

    Wagner, Nils-Frederic; Chaves, Pedro; Wolff, Annemarie

    2017-06-01

    In this article we critically review the neural mechanisms of moral cognition that have recently been studied via electroencephalography (EEG). Such studies promise to shed new light on traditional moral questions by helping us to understand how effective moral cognition is embodied in the brain. It has been argued that conflicting normative ethical theories require different cognitive features and can, accordingly, in a broadly conceived naturalistic attempt, be associated with different brain processes that are rooted in different brain networks and regions. This potentially morally relevant brain activity has been empirically investigated through EEG-based studies on moral cognition. From neuroscientific evidence gathered in these studies, a variety of normative conclusions have been drawn and bioethical applications have been suggested. We discuss methodological and theoretical merits and demerits of the attempt to use EEG techniques in a morally significant way, point to legal challenges and policy implications, indicate the potential to reveal biomarkers of psychopathological conditions, and consider issues that might inform future bioethical work.

  12. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1991-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  13. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1992-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  14. Cognitive methodology for forecasting oil and gas industry using pattern-based neural information technologies

    Science.gov (United States)

    Gafurov, O.; Gafurov, D.; Syryamkin, V.

    2018-05-01

    The paper analyses a field of computer science formed at the intersection of such areas of natural science as artificial intelligence, mathematical statistics, and database theory, which is referred to as "Data Mining" (discovery of knowledge in data). The theory of neural networks is applied along with classical methods of mathematical analysis and numerical simulation. The paper describes the technique protected by the patent of the Russian Federation for the invention “A Method for Determining Location of Production Wells during the Development of Hydrocarbon Fields” [1–3] and implemented using the geoinformation system NeuroInformGeo. There are no analogues in domestic and international practice. The paper gives an example of comparing the forecast of the oil reservoir quality made by the geophysicist interpreter using standard methods and the forecast of the oil reservoir quality made using this technology. The technical result achieved shows the increase of efficiency, effectiveness, and ecological compatibility of development of mineral deposits and discovery of a new oil deposit.

  15. The Cognitive and Neural Expression of Semantic Memory Impairment in Mild Cognitive Impairment and Early Alzheimer's Disease

    Science.gov (United States)

    Joubert, Sven; Brambati, Simona M.; Ansado, Jennyfer; Barbeau, Emmanuel J.; Felician, Olivier; Didic, Mira; Lacombe, Jacinthe; Goldstein, Rachel; Chayer, Celine; Kergoat, Marie-Jeanne

    2010-01-01

    Semantic deficits in Alzheimer's disease have been widely documented, but little is known about the integrity of semantic memory in the prodromal stage of the illness. The aims of the present study were to: (i) investigate naming abilities and semantic memory in amnestic mild cognitive impairment (aMCI), early Alzheimer's disease (AD) compared to…

  16. Systems biological approach to investigate the lack of familial link between Down's Syndrome & Neural Tube Disorders.

    Science.gov (United States)

    Ragunath, Pk; Abhinand, Pa

    2013-01-01

    Systems Biology involves the study of the interactions of biological systems and ultimately their functions. Down's syndrome (DS) is one of the most common genetic disorders which are caused by complete, or occasionally partial, triplication of chromosome 21, characterized by cognitive and language dysfunction coupled with sensory and neuromotor deficits. Neural Tube Disorders (NTDs) are a group of congenital malformations of the central nervous system and neighboring structures related to defective neural tube closure during the first trimester of pregnancy usually occurring between days 18-29 of gestation. Several studies in the past have provided considerable evidence that abnormal folate and methyl metabolism are associated with onset of DS & NTDs. There is a possible common etiological pathway for both NTDs and Down's syndrome. But, various research studies over the years have indicated very little evidence for familial link between the two disorders. Our research aimed at the gene expression profiling of microarray datasets pertaining to the two disorders to identify genes whose expression levels are significantly altered in these conditions. The genes which were 1.5 fold unregulated and having a p-value disorders were recognized and over representation analysis was carried out for each of the constituent genes. The comprehensive manual analysis of these genes yields a hypothetical understanding of the lack of familial link between DS and NTDs. There were no genes involved with folic acid present in the dense cliques. Only - CBL, EGFR genes were commonly present, which makes the allelic variants of these genes - good candidates for future studies regarding the familial link between DS and NTDs. NTD - Neural Tube Disorders, DS - Down's Syndrome, MTHFR - Methylenetetrahydrofolate reductase, MTRR- 5 - methyltetrahydrofolate-homocysteine methyltransferase reductase.

  17. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  18. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  19. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  20. Optimizing Markovian modeling of chaotic systems with recurrent neural networks

    International Nuclear Information System (INIS)

    Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

    2008-01-01

    In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

  1. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  2. Neural Dynamics of Multiple Object Processing in Mild Cognitive Impairment and Alzheimer's Disease: Future Early Diagnostic Biomarkers?

    Science.gov (United States)

    Bagattini, Chiara; Mazza, Veronica; Panizza, Laura; Ferrari, Clarissa; Bonomini, Cristina; Brignani, Debora

    2017-01-01

    The aim of this study was to investigate the behavioral and electrophysiological dynamics of multiple object processing (MOP) in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to test whether its neural signatures may represent reliable diagnostic biomarkers. Behavioral performance and event-related potentials [N2pc and contralateral delay activity (CDA)] were measured in AD, MCI, and healthy controls during a MOP task, which consisted in enumerating a variable number of targets presented among distractors. AD patients showed an overall decline in accuracy for both small and large target quantities, whereas in MCI patients, only enumeration of large quantities was impaired. N2pc, a neural marker of attentive individuation, was spared in both AD and MCI patients. In contrast, CDA, which indexes visual short term memory abilities, was altered in both groups of patients, with a non-linear pattern of amplitude modulation along the continuum of the disease: a reduction in AD and an increase in MCI. These results indicate that AD pathology shows a progressive decline in MOP, which is associated to the decay of visual short-term memory mechanisms. Crucially, CDA may be considered as a useful neural signature both to distinguish between healthy and pathological aging and to characterize the different stages along the AD continuum, possibly becoming a reliable candidate for an early diagnostic biomarker of AD pathology.

  3. Neural correlates of emotion?cognition interactions: A review of evidence from brain imaging investigations

    OpenAIRE

    Dolcos, Florin; Iordan, Alexandru D.; Dolcos, Sanda

    2011-01-01

    Complex dynamic behaviour involves reciprocal influences between emotion and cognition. On the one hand, emotion is a ?double-edged sword? that may affect various aspects of our cognition and behaviour, by enhancing or hindering them and exerting both transient and long-term influences. On the other hand, emotion processing is also susceptible to cognitive influences, typically exerted in the form of emotion regulation. Noteworthy, both of these reciprocal influences are subjective to individ...

  4. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  5. Cooperative and Competitive Contextual Effects on Social Cognitive and Empathic Neural Responses

    Directory of Open Access Journals (Sweden)

    Minhye Lee

    2018-06-01

    Full Text Available We aimed to differentiate the neural responses to cooperative and competitive contexts, which are the two of the most important social contexts in human society. Healthy male college students were asked to complete a Tetris-like task requiring mental rotation skills under individual, cooperative, and competitive contexts in an fMRI scanner. While the participants completed the task, pictures of others experiencing pain evoking emotional empathy randomly appeared to capture contextual effects on empathic neural responses. Behavioral results indicated that, in the presence of cooperation, participants solved the tasks more accurately and quickly than what they did when in the presence of competition. The fMRI results revealed activations in the dorsolateral prefrontal cortex (dlPFC and dorsomedial prefrontal cortex (dmPFC related to executive functions and theory of mind when participants performed the task under both cooperative and competitive contexts, whereas no activation of such areas was observed in the individual context. Cooperation condition exhibited stronger neural responses in the ventromedial prefrontal cortex (vmPFC and dmPFC than competition condition. Competition condition, however, showed marginal neural responses in the cerebellum and anterior insular cortex (AIC. The two social contexts involved stronger empathic neural responses to other’s pain than the individual context, but no substantial differences between cooperation and competition were present. Regions of interest analyses revealed that individual’s trait empathy modulated the neural activity in the state empathy network, the AIC, and the dorsal anterior cingulate cortex (dACC depending on the social context. These results suggest that cooperation improves task performance and activates neural responses associated with reward and mentalizing. Furthermore, the interaction between trait- and state-empathy was explored by correlation analyses between individual

  6. Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording Systems

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2013-01-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5-μm standard CMOS process and consumes 4.5 mW from ±1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of ~ 2.26 Mb/s. PMID:19497823

  7. Distributed Cognition and Distributed Morality: Agency, Artifacts and Systems.

    Science.gov (United States)

    Heersmink, Richard

    2017-04-01

    There are various philosophical approaches and theories describing the intimate relation people have to artifacts. In this paper, I explore the relation between two such theories, namely distributed cognition and distributed morality theory. I point out a number of similarities and differences in these views regarding the ontological status they attribute to artifacts and the larger systems they are part of. Having evaluated and compared these views, I continue by focussing on the way cognitive artifacts are used in moral practice. I specifically conceptualise how such artifacts (a) scaffold and extend moral reasoning and decision-making processes, (b) have a certain moral status which is contingent on their cognitive status, and (c) whether responsibility can be attributed to distributed systems. This paper is primarily written for those interested in the intersection of cognitive and moral theory as it relates to artifacts, but also for those independently interested in philosophical debates in extended and distributed cognition and ethics of (cognitive) technology.

  8. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  9. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  10. Fun cube based brain gym cognitive function assessment system.

    Science.gov (United States)

    Zhang, Tao; Lin, Chung-Chih; Yu, Tsang-Chu; Sun, Jing; Hsu, Wen-Chuin; Wong, Alice May-Kuen

    2017-05-01

    The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment. Three kinds of experiments were performed to evaluate the developed FC-based BG cognitive function assessment system. The experimental results showed that the Pearson correlation coefficient between the system's evaluation outcomes and the traditional Montreal Cognitive Assessment scores was 0.83. The average Technology Acceptance Model 2 score was close to six for 31 elderly subjects. Most subjects considered that the brain games are interesting and the FC human-machine interface is easy to learn and operate. The control group and the cognitive impairment group had statistically significant difference with respect to the accuracy of and the time taken for the brain cognitive function assessment games, including Animal Naming, Color Search, Trail Making Test, Change Blindness, and Forward / Backward Digit Span. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Neural Mechanism of Cognitive Control Impairment in Patients with Hepatic Cirrhosis: A Functional Magnetic Resonance Imaging Study

    International Nuclear Information System (INIS)

    Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi

    2007-01-01

    Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients

  12. Neural Mechanism of Cognitive Control Impairment in Patients with Hepatic Cirrhosis: A Functional Magnetic Resonance Imaging Study

    Energy Technology Data Exchange (ETDEWEB)

    Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi [Dept. of Radiology, Tianjin First Central Hospital of Tianjin Medical Univ, Tianjin (China)

    2007-07-15

    Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients.

  13. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  14. Erythropoietin reduces neural and cognitive processing of fear in human models of antidepressant drug action

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla; O'Sullivan, Ursula; Harmer, Catherine J

    2007-01-01

    with reduced attention to fear. Erythropoietin additionally reduced recognition of fearful facial expressions without affecting recognition of other emotional expressions. These actions occurred in the absence of changes in hematological parameters. CONCLUSIONS: The present study demonstrates that Epo directly......) versus saline on the neural processing of happy and fearful faces in 23 healthy volunteers. Facial expression recognition was assessed outside the scanner. RESULTS: One week after administration, Epo reduced neural response to fearful versus neutral faces in the occipito-parietal cortex consistent...... study aimed to explore the effects of Epo on neural and behavioral measures of emotional processing relevant for depression and the effects of conventional antidepressant medication. METHODS: In the present study, we used functional magnetic resonance imaging to explore the effects of Epo (40,000 IU...

  15. Cognitive phase transitions in the cerebral cortex enhancing the neuron doctrine by modeling neural fields

    CERN Document Server

    Kozma, Robert

    2016-01-01

    This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets).  The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.

  16. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    DEFF Research Database (Denmark)

    Allen, Micah Galen

    Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through...... prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending...

  17. Acute Effects of Glucose and Fructose Administration on the Neural Correlates of Cognitive Functioning in Healthy Subjects: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Davide Zanchi

    2018-03-01

    Full Text Available The present randomized double-blinded cross-over study aims to extensively study the neural correlates underpinning cognitive functions in healthy subjects after acute glucose and fructose administration, using an integrative multimodal neuroimaging approach. Five minutes after glucose, fructose, or placebo administration through a nasogastric tube, 12 participants underwent 3 complementary neuroimaging techniques: 2 task-based functional magnetic resonance imaging (fMRI sequences to assess working memory (N-back and response inhibition (Go/No-Go and one resting state fMRI sequence to address the cognition-related fronto-parietal network (FPN and salience network (SN. During working memory processing, glucose intake decreased activation in the anterior cingulate cortex (ACC relative to placebo, while fructose decreased activation in the ACC and sensory cortex relative to placebo and glucose. During response inhibition, glucose and fructose decreased activation in the ACC, insula and visual cortex relative to placebo. Resting state fMRI indicated increased global connectivity strength of the FPN and the SN during glucose and fructose intake. The results demonstrate that glucose and fructose lead to partially different partially overlapping changes in regional brain activities that underpin cognitive performance in different tasks.

  18. Sex-specific neural activity when resolving cognitive interference in individuals with or without prior internalizing disorders.

    Science.gov (United States)

    Wang, Zhishun; Jacobs, Rachel H; Marsh, Rachel; Horga, Guillermo; Qiao, Jianping; Warner, Virginia; Weissman, Myrna M; Peterson, Bradley S

    2016-03-30

    The processing of cognitive interference is a self-regulatory capacity that is impaired in persons with internalizing disorders. This investigation was to assess sex differences in the neural correlates of cognitive interference in individuals with and without an illness history of an internalizing disorder. We compared functional magnetic resonance imaging blood-oxygenation-level-dependent responses in both males (n=63) and females (n=80) with and without this illness history during performance of the Simon task. Females deactivated superior frontal gyrus, inferior parietal lobe, and posterior cingulate cortex to a greater extent than males. Females with a prior history of internalizing disorder also deactivated these regions more compared to males with that history, and they additionally demonstrated greater activation of right inferior frontal gyrus. These group differences were represented in a significant sex-by-illness interaction in these regions. These deactivated regions compose a task-negative or default mode network, whereas the inferior frontal gyrus usually activates when performing an attention-demanding task and is a key component of a task-positive network. Our findings suggest that a prior history of internalizing disorders disproportionately influences functioning of the default mode network and is associated with an accompanying activation of the task-positive network in females during the resolution of cognitive interference. Copyright © 2016. Published by Elsevier Ireland Ltd.

  19. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

    Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

  20. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  1. 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.

  2. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  3. The exploitation of neural networks in automotive engine management systems

    Energy Technology Data Exchange (ETDEWEB)

    Shayler, P.J.; Goodman, M. [University of Nottingham (United Kingdom); Ma, T. [Ford Motor Company, Dagenham (United Kingdom). Research and Engineering Centre

    2000-07-01

    The use of electronic engine control systems on spark ignition engines has enabled a high degree of performance optimisation to be achieved. The range of functions performed by these systems, and the level of performance demanded, is rising and thus so are development times and costs. Neural networks have attracted attention as having the potential to simplify software development and improve the performance of this software. The scope and nature of possible applications is described. In particular, the pattern recognition and classification abilities of networks are applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities are investigated as an alternative to large 'lookup' tables and calibration functions. (author)

  4. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  5. Towards Energy Efficient Cognitive Radio Systems

    KAUST Repository

    Alabbasi, AbdulRahman

    2016-07-14

    Cognitive radio (CR) is a cutting-edge wireless communication technology that adopts several existing communication concepts in order to efficiently utilize the spectrum and meet the users demands of high throughput and real-time systems. Conventionally, high throughput demands are met through adopting broadband and multi-antenna technologies such as, orthogonal frequency division multiplexing (OFDM) and Multi-Input Multi-Output (MIMO). Whereas, real-time application demands are met by analyzing metrics which characterize the delay limited channels, such as, outage probability over block-fading channels. Being an environmental friendly technology, energy efficiency metrics should be considered in the design of a CR application. This thesis tackles the energy efficiency of CR system from different aspects, utilizing different measuring metrics and constrains. Under the single-input single-output (SISO) OFDM we minimized the energy per goodbit (EPG) metric subject to several power and Quality of Service (QoS) constraints. In this approach, the minimum EPG metric is optimized via proposing two optimal and sub-optimal resource allocation schemes. We consider several parameters as optimization variables, such as, power policy, sensing threshold, and channel quality threshold. We also captured the impact of involving the media access control (MAC) layers parameters, such as, frame length, in the minimization of a modified EPG metric. Also, a MAC protocol, i.e., hybrid automatic repeat request (HARQ), and the associated power consumption of the retransmission mechanism is considered in the formulation of the problem. In this context, the optimal power and frame length are derived to minimize the modified EPG while considering several spectrum-sharing scenarios, which depend on sensing information. In MIMO based CR system, we maximized capacity to power ratio (CPR) (as an energy efficiency (EE) metric) subject to several power and QoS constraints. In this context, the

  6. Direct process estimation from tomographic data using artificial neural systems

    Science.gov (United States)

    Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

    2001-07-01

    The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

  7. Supporting Multiple Cognitive Processing Styles Using Tailored Support Systems

    International Nuclear Information System (INIS)

    Tuan Q. Tran; Karen M. Feigh; Amy R. Pritchett

    2007-01-01

    According to theories of cognitive processing style or cognitive control mode, human performance is more effective when an individual's cognitive state (e.g., intuition/scramble vs. deliberate/strategic) matches his/her ecological constraints or context (e.g., utilize intuition to strive for a 'good-enough' response instead of deliberating for the 'best' response under high time pressure). Ill-mapping between cognitive state and ecological constraints are believed to lead to degraded task performance. Consequently, incorporating support systems which are designed to specifically address multiple cognitive and functional states e.g., high workload, stress, boredom, and initiate appropriate mitigation strategies (e.g., reduce information load) is essential to reduce plant risk. Utilizing the concept of Cognitive Control Models, this paper will discuss the importance of tailoring support systems to match an operator's cognitive state, and will further discuss the importance of these ecological constraints in selecting and implementing mitigation strategies for safe and effective system performance. An example from the nuclear power plant industry illustrating how a support system might be tailored to support different cognitive states is included

  8. An Oversampled Filter Bank Multicarrier System for cognitive Radio

    NARCIS (Netherlands)

    Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria; Zhang, Q; Zhang, Q.

    2008-01-01

    Due to small sideband power leakage, filter bank multicarrier techniques are considered as interesting alternatives to traditional OFDMs for spectrum pooling Cognitive Radio. In this paper, we propose an oversampled filter bank multicarrier system for Cognitive Radio. The increased spacing between

  9. 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.

  10. Plastic reorganization of neural systems for perception of others in the congenitally blind.

    Science.gov (United States)

    Fairhall, S L; Porter, K B; Bellucci, C; Mazzetti, M; Cipolli, C; Gobbini, M I

    2017-09-01

    Recent evidence suggests that the function of the core system for face perception might extend beyond visual face-perception to a broader role in person perception. To critically test the broader role of core face-system in person perception, we examined the role of the core system during the perception of others in 7 congenitally blind individuals and 15 sighted subjects by measuring their neural responses using fMRI while they listened to voices and performed identity and emotion recognition tasks. We hypothesised that in people who have had no visual experience of faces, core face-system areas may assume a role in the perception of others via voices. Results showed that emotions conveyed by voices can be decoded in homologues of the core face system only in the blind. Moreover, there was a specific enhancement of response to verbal as compared to non-verbal stimuli in bilateral fusiform face areas and the right posterior superior temporal sulcus showing that the core system also assumes some language-related functions in the blind. These results indicate that, in individuals with no history of visual experience, areas of the core system for face perception may assume a role in aspects of voice perception that are relevant to social cognition and perception of others' emotions. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  11. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  12. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  13. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  14. Ethical principles and guidelines for the development of cognitive systems.

    Energy Technology Data Exchange (ETDEWEB)

    Shaneyfelt, Wendy

    2006-05-01

    As cognitive systems technologies emerge, so too do the ethical issues surrounding their development and use. To develop cognitive systems technologies responsibly, Sandia National Laboratories is establishing a framework to proactively address both real and potential ethical issues. This report contains the principles and guidelines developers can use to guide them as they are confronted with ethical issues related to developing cognitive systems technologies as they apply to U.S. national security. A process to apply these principles offers a practical way to transfer these principles from paper to a working strategy. Case studies are presented to reflect upon potential scenarios and to consider resolution strategies.

  15. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  16. System structure and cognitive ability as predictors of performance in dynamic system control tasks

    Directory of Open Access Journals (Sweden)

    Jan Hundertmark

    2015-12-01

    Full Text Available In dynamic system control, cognitive mechanisms and abilities underlying performance may vary depending on the nature of the task. We therefore investigated the effects of system structure and its interaction with cognitive abilities on system control performance. A sample of 127 university students completed a series of different system control tasks that were manipulated in terms of system size and recurrent feedback, either with or without a cognitive load manipulation. Cognitive abilities assessed included reasoning ability, working memory capacity, and cognitive reflection. System size and recurrent feedback affected overall performance as expected. Overall, the results support that cognitive ability is a good predictor of performance in dynamic system control tasks but predictiveness is reduced when the system structure contains recurrent feedback. We discuss this finding from a cognitive processing perspective as well as its implications for individual differences research in dynamic systems.

  17. Complex systems and health behavior change: insights from cognitive science.

    Science.gov (United States)

    Orr, Mark G; Plaut, David C

    2014-05-01

    To provide proof-of-concept that quantum health behavior can be instantiated as a computational model that is informed by cognitive science, the Theory of Reasoned Action, and quantum health behavior theory. We conducted a synthetic review of the intersection of quantum health behavior change and cognitive science. We conducted simulations, using a computational model of quantum health behavior (a constraint satisfaction artificial neural network) and tested whether the model exhibited quantum-like behavior. The model exhibited clear signs of quantum-like behavior. Quantum health behavior can be conceptualized as constraint satisfaction: a mitigation between current behavioral state and the social contexts in which it operates. We outlined implications for moving forward with computational models of both quantum health behavior and health behavior in general.

  18. Human neural stem cells over-expressing choline acetyltransferase restore cognition in rat model of cognitive dysfunction.

    Science.gov (United States)

    Park, Dongsun; Lee, Hong Jun; Joo, Seong Soo; Bae, Dae-Kwon; Yang, Goeun; Yang, Yun-Hui; Lim, Inja; Matsuo, Akinori; Tooyama, Ikuo; Kim, Yun-Bae; Kim, Seung U

    2012-04-01

    A human neural stem cell (NSC) line over-expressing human choline acetyltransferase (ChAT) gene was generated and these F3.ChAT NSCs were transplanted into the brain of rat Alzheimer disease (AD) model which was induced by application of ethylcholine mustard aziridinium ion (AF64A) that specifically denatures cholinergic nerves and thereby leads to memory deficit as a salient feature of AD. Transplantation of F3.ChAT human NSCs fully recovered the learning and memory function of AF64A animals, and induced elevated levels of acetylcholine (ACh) in cerebrospinal fluid (CSF). Transplanted F3.ChAT human NSCs were found to migrate to various brain regions including cerebral cortex, hippocampus, striatum and septum, and differentiated into neurons and astrocytes. The present study demonstrates that brain transplantation of human NSCs over-expressing ChAT ameliorates complex learning and memory deficits in AF64A-cholinotoxin-induced AD rat model. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Impairments in cognition and neural precursor cell proliferation in mice expressing constitutively active glycogen synthase kinase-3

    Directory of Open Access Journals (Sweden)

    Marta ePardo

    2015-03-01

    Full Text Available ABSTRACTBrain glycogen synthase kinase-3 (GSK3 is hyperactive in several neurological conditions that involve impairments in both cognition and neurogenesis. This raises the hypotheses that hyperactive GSK3 may directly contribute to impaired cognition, and that this may be related to deficiencies in neural precursor cells (NPC. To study the effects of hyperactive GSK3 in the absence of disease influences, we compared adult hippocampal NPC proliferation and performance in three cognitive tasks in male and female wild-type mice and GSK3 knockin mice, which express constitutively active GSK3. NPC proliferation was ~40% deficient in both male and female GSK3 knockin mice compared with wild-type mice. Environmental enrichment (EE increased NPC proliferation in male, but not female, GSK3 knockin mice and wild-type mice. Male and female GSK3 knockin mice exhibited impairments in novel object recognition, temporal order memory, and coordinate spatial processing compared with gender-matched wild-type mice. EE restored impaired novel object recognition and temporal ordering in both sexes of GSK3 knockin mice, indicating that this repair was not dependent on NPC proliferation, which was not increased by EE in female GSK3 knockin mice. Acute 1 hr pretreatment with the GSK3 inhibitor TDZD-8 also improved novel object recognition and temporal ordering in male and female GSK3 knockin mice. These findings demonstrate that hyperactive GSK3 is sufficient to impair adult hippocampal NPC proliferation and to impair performance in three cognitive tasks in both male and female mice, but these changes in NPC proliferation do not directly regulate novel object recognition and temporal ordering tasks.

  20. The neural correlates of visuo-spatial working memory in children with autism spectrum disorder: effects of cognitive load.

    Science.gov (United States)

    Vogan, Vanessa M; Morgan, Benjamin R; Lee, Wayne; Powell, Tamara L; Smith, Mary Lou; Taylor, Margot J

    2014-01-01

    Research on the neural bases of cognitive deficits in autism spectrum disorder (ASD) has shown that working memory (WM) difficulties are associated with abnormalities in the prefrontal cortex. However, cognitive load impacts these findings, and no studies have examined the relation between WM load and neural underpinnings in children with ASD. Thus, the current study determined the effects of cognitive load on WM, using a visuo-spatial WM capacity task in children with and without ASD with functional magnetic resonance imaging (fMRI). We used fMRI and a 1-back colour matching task (CMT) task with four levels of difficulty to compare the cortical activation patterns associated with WM in children (7-13 years old) with high functioning autism (N = 19) and matched controls (N = 17) across cognitive load. Performance on CMT was comparable between groups, with the exception of one difficulty level. Using linear trend analyses, the control group showed increasing activation as a function of difficulty level in frontal and parietal lobes, particularly between the highest difficulty levels, and decreasing activation as a function of difficulty level in the posterior cingulate and medial frontal gyri. In contrast, children with ASD showed increasing activation only in posterior brain regions and decreasing activation in the posterior cingulate and medial frontal gyri, as a function of difficulty level. Significant differences were found in the precuneus, dorsolateral prefrontal cortex and medial premotor cortex, where control children showed greater positive linear relations between cortical activity and task difficulty level, particularly at the highest difficulty levels, but children with ASD did not show these trends. Children with ASD showed differences in activation in the frontal and parietal lobes-both critical substrates for visuo-spatial WM. Our data suggest that children with ASD rely mainly on posterior brain regions associated with visual and lower level

  1. 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.

  2. The influence of combined cognitive plus social-cognitive training on amygdala response during face emotion recognition in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; D'Esposito, Mark; Vinogradov, Sophia

    2013-08-30

    Both cognitive and social-cognitive deficits impact functional outcome in schizophrenia. Cognitive remediation studies indicate that targeted cognitive and/or social-cognitive training improves behavioral performance on trained skills. However, the neural effects of training in schizophrenia and their relation to behavioral gains are largely unknown. This study tested whether a 50-h intervention which included both cognitive and social-cognitive training would influence neural mechanisms that support social ccognition. Schizophrenia participants completed a computer-based intervention of either auditory-based cognitive training (AT) plus social-cognition training (SCT) (N=11) or non-specific computer games (CG) (N=11). Assessments included a functional magnetic resonance imaging (fMRI) task of facial emotion recognition, and behavioral measures of cognition, social cognition, and functional outcome. The fMRI results showed the predicted group-by-time interaction. Results were strongest for emotion recognition of happy, surprise and fear: relative to CG participants, AT+SCT participants showed a neural activity increase in bilateral amygdala, right putamen and right medial prefrontal cortex. Across all participants, pre-to-post intervention neural activity increase in these regions predicted behavioral improvement on an independent emotion perception measure (MSCEIT: Perceiving Emotions). Among AT+SCT participants alone, neural activity increase in right amygdala predicted behavioral improvement in emotion perception. The findings indicate that combined cognition and social-cognition training improves neural systems that support social-cognition skills. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  3. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  4. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

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

    Science.gov (United States)

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

    2011-02-25

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

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ricardo Cáceda

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

  8. Cognitive Models for Learning to Control Dynamic Systems

    National Research Council Canada - National Science Library

    Eberhart, Russ; Hu, Xiaohui; Chen, Yaobin

    2008-01-01

    Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning...

  9. Location-based resource allocation for OFDMA cognitive radio systems

    KAUST Repository

    Nam, Haewoon; Ben Ghorbel, Mahdi; Alouini, Mohamed-Slim

    2010-01-01

    In cognitive radio systems, in order for the secondary users to opportunistically share the spectrum without interfering the primary users, an accurate spectrum measurement and a precise estimation of the interference at the primary users

  10. Culture and cognition in health systems change.

    Science.gov (United States)

    Evans, Jenna M; Baker, G Ross; Berta, Whitney; Barnsley, Jan

    2015-01-01

    Large-scale change involves modifying not only the structures and functions of multiple organizations, but also the mindsets and behaviours of diverse stakeholders. This paper focuses on the latter: the informal, less visible, and often neglected psychological and social factors implicated in change efforts. The purpose of this paper is to differentiate between the concepts of organizational culture and mental models, to argue for the value of applying a shared mental models (SMM) framework to large-scale change, and to suggest directions for future research. The authors provide an overview of SMM theory and use it to explore the dynamic relationship between culture and cognition. The contributions and limitations of the theory to change efforts are also discussed. Culture and cognition are complementary perspectives, providing insight into two different levels of the change process. SMM theory draws attention to important questions that add value to existing perspectives on large-scale change. The authors outline these questions for future research and argue that research and practice in this domain may be best served by focusing less on the potentially narrow goal of "achieving consensus" and more on identifying, understanding, and managing cognitive convergences and divergences as part of broader research and change management programmes. Drawing from both cultural and cognitive paradigms can provide researchers with a more complete picture of the processes by which coordinated action are achieved in complex change initiatives in the healthcare domain.

  11. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  12. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  13. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  14. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

  15. Convergent dysregulation of frontal cortical cognitive and reward systems in eating disorders.

    Science.gov (United States)

    Stefano, George B; Ptáček, Radek; Kuželová, Hana; Mantione, Kirk J; Raboch, Jiří; Papezova, Hana; Kream, Richard M

    2013-05-10

    A substantive literature has drawn a compelling case for the functional involvement of mesolimbic/prefrontal cortical neural reward systems in normative control of eating and in the etiology and persistence of severe eating disorders that affect diverse human populations. Presently, we provide a short review that develops an equally compelling case for the importance of dysregulated frontal cortical cognitive neural networks acting in concert with regional reward systems in the regulation of complex eating behaviors and in the presentation of complex pathophysiological symptoms associated with major eating disorders. Our goal is to highlight working models of major eating disorders that incorporate complementary approaches to elucidate functionally interactive neural circuits defined by their regulatory neurochemical phenotypes. Importantly, we also review evidence-based linkages between widely studied psychiatric and neurodegenerative syndromes (e.g., autism spectrum disorders and Parkinson's disease) and co-morbid eating disorders to elucidate basic mechanisms involving dopaminergic transmission and its regulation by endogenously expressed morphine in these same cortical regions.

  16. Radio frequency integrated circuit design for cognitive radio systems

    CERN Document Server

    Fahim, Amr

    2015-01-01

    This book fills a disconnect in the literature between Cognitive Radio systems and a detailed account of the circuit implementation and architectures required to implement such systems.  Throughout the book, requirements and constraints imposed by cognitive radio systems are emphasized when discussing the circuit implementation details.  In addition, this book details several novel concepts that advance state-of-the-art cognitive radio systems.  This is a valuable reference for anybody with background in analog and radio frequency (RF) integrated circuit design, needing to learn more about integrated circuits requirements and implementation for cognitive radio systems. ·         Describes in detail cognitive radio systems, as well as the circuit implementation and architectures required to implement them; ·         Serves as an excellent reference to state-of-the-art wideband transceiver design; ·         Emphasizes practical requirements and constraints imposed by cognitive radi...

  17. The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective

    Directory of Open Access Journals (Sweden)

    Joseph Billingsley

    2017-05-01

    Full Text Available Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model’s claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part.

  18. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  19. A surety engineering framework to reduce cognitive systems risks.

    Energy Technology Data Exchange (ETDEWEB)

    Caudell, Thomas P. (University of New Mexico, Albuquerque, NM); Peercy, David Eugene; Caldera, Eva O. (University of New Mexico, Albuquerque, NM); Shaneyfelt, Wendy L.

    2008-12-01

    Cognitive science research investigates the advancement of human cognition and neuroscience capabilities. Addressing risks associated with these advancements can counter potential program failures, legal and ethical issues, constraints to scientific research, and product vulnerabilities. Survey results, focus group discussions, cognitive science experts, and surety researchers concur technical risks exist that could impact cognitive science research in areas such as medicine, privacy, human enhancement, law and policy, military applications, and national security (SAND2006-6895). This SAND report documents a surety engineering framework and a process for identifying cognitive system technical, ethical, legal and societal risks and applying appropriate surety methods to reduce such risks. The framework consists of several models: Specification, Design, Evaluation, Risk, and Maturity. Two detailed case studies are included to illustrate the use of the process and framework. Several Appendices provide detailed information on existing cognitive system architectures; ethical, legal, and societal risk research; surety methods and technologies; and educing information research with a case study vignette. The process and framework provide a model for how cognitive systems research and full-scale product development can apply surety engineering to reduce perceived and actual risks.

  20. Cognitive performance modeling based on general systems performance theory.

    Science.gov (United States)

    Kondraske, George V

    2010-01-01

    General Systems Performance Theory (GSPT) was initially motivated by problems associated with quantifying different aspects of human performance. It has proved to be invaluable for measurement development and understanding quantitative relationships between human subsystem capacities and performance in complex tasks. It is now desired to bring focus to the application of GSPT to modeling of cognitive system performance. Previous studies involving two complex tasks (i.e., driving and performing laparoscopic surgery) and incorporating measures that are clearly related to cognitive performance (information processing speed and short-term memory capacity) were revisited. A GSPT-derived method of task analysis and performance prediction termed Nonlinear Causal Resource Analysis (NCRA) was employed to determine the demand on basic cognitive performance resources required to support different levels of complex task performance. This approach is presented as a means to determine a cognitive workload profile and the subsequent computation of a single number measure of cognitive workload (CW). Computation of CW may be a viable alternative to measuring it. Various possible "more basic" performance resources that contribute to cognitive system performance are discussed. It is concluded from this preliminary exploration that a GSPT-based approach can contribute to defining cognitive performance models that are useful for both individual subjects and specific groups (e.g., military pilots).

  1. Long-Term Effects of Chronic Oral Ritalin Administration on Cognitive and Neural Development in Adolescent Wistar Kyoto Rats

    Directory of Open Access Journals (Sweden)

    Jennifer L. Cornish

    2012-09-01

    Full Text Available The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD often results in chronic treatment with psychostimulants such as methylphenidate (MPH, Ritalin®. With increases in misdiagnosis of ADHD, children may be inappropriately exposed to chronic psychostimulant treatment during development. The aim of this study was to assess the effect of chronic Ritalin treatment on cognitive and neural development in misdiagnosed “normal” (Wistar Kyoto, WKY rats and in Spontaneously Hypertensive Rats (SHR, a model of ADHD. Adolescent male animals were treated for four weeks with oral Ritalin® (2 × 2 mg/kg/day or distilled water (dH2O. The effect of chronic treatment on delayed reinforcement tasks (DRT and tyrosine hydroxylase immunoreactivity (TH-ir in the prefrontal cortex was assessed. Two weeks following chronic treatment, WKY rats previously exposed to MPH chose the delayed reinforcer significantly less than the dH2O treated controls in both the DRT and extinction task. MPH treatment did not significantly alter cognitive performance in the SHR. TH-ir in the infralimbic cortex was significantly altered by age and behavioural experience in WKY and SHR, however this effect was not evident in WKY rats treated with MPH. These results suggest that chronic treatment with MPH throughout adolescence in “normal” WKY rats increased impulsive choice and altered catecholamine development when compared to vehicle controls.

  2. Training the brain: practical applications of neural plasticity from the intersection of cognitive neuroscience, developmental psychology, and prevention science.

    Science.gov (United States)

    Bryck, Richard L; Fisher, Philip A

    2012-01-01

    Prior researchers have shown that the brain has a remarkable ability for adapting to environmental changes. The positive effects of such neural plasticity include enhanced functioning in specific cognitive domains and shifts in cortical representation following naturally occurring cases of sensory deprivation; however, maladaptive changes in brain function and development owing to early developmental adversity and stress have also been well documented. Researchers examining enriched rearing environments in animals have revealed the potential for inducing positive brain plasticity effects and have helped to popularize methods for training the brain to reverse early brain deficits or to boost normal cognitive functioning. In this article, two classes of empirically based methods of brain training in children are reviewed and critiqued: laboratory-based, mental process training paradigms and ecological interventions based upon neurocognitive conceptual models. Given the susceptibility of executive function disruption, special attention is paid to training programs that emphasize executive function enhancement. In addition, a third approach to brain training, aimed at tapping into compensatory processes, is postulated. Study results showing the effectiveness of this strategy in the field of neurorehabilitation and in terms of naturally occurring compensatory processing in human aging lend credence to the potential of this approach. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

  3. Cognitive, emotional, and neural benefits of musical leisure activities in aging and neurological rehabilitation: A critical review.

    Science.gov (United States)

    Särkämö, Teppo

    2017-04-28

    Music has the capacity to engage auditory, cognitive, motor, and emotional functions across cortical and subcortical brain regions and is relatively preserved in aging and dementia. Thus, music is a promising tool in the rehabilitation of aging-related neurological illnesses, such as stroke and Alzheimer disease. As the population ages and the incidence and prevalence of these illnesses rapidly increases, music-based interventions that are enjoyable and effective in the everyday care of the patients are needed. In addition to formal music therapy, musical leisure activities, such as music listening and singing, which patients can do on their own or with a caregiver, are a promising way to support psychological well-being during aging and in neurological rehabilitation. This review article provides an overview of current evidence on the cognitive, emotional, and neural effects of musical leisure activities both during normal aging and in the rehabilitation and care of stroke patients and people with dementia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  4. Age-Related Differences in Neural Recruitment During the Use of Cognitive Reappraisal and Selective Attention as Emotion Regulation Strategies

    Directory of Open Access Journals (Sweden)

    Eric S Allard

    2014-04-01

    Full Text Available The present study examined age differences in the timing and neural recruitment within lateral and medial PFC while younger and older adults hedonically regulated their responses to unpleasant film clips. When analyses focused on activity during the emotional peak of the film clip (the most emotionally salient portion of the film, several age differences emerged. When comparing regulation to passive viewing (combined effects of selective attention and reappraisal younger adults showed greater regulation related activity in lateral PFC (DLPFC, VLPFC, OFC and medial PFC (ACC while older adults showed greater activation within a region DLPFC. When assessing distinct effects of the regulation conditions, an ANOVA revealed a significant Age X Regulation Condition interaction within bilateral DLPFC and ACC; older adults but not young adults showed greater recruitment within these regions for reappraisal than selective attention. When examining activity at the onset of the film clip and at its emotional peak, the timing of reappraisal-related activity within VLPFC differed between age groups: Younger adults showed greater activity at film onset while older adults showed heightened activity during the peak. Our results suggest that older adults rely more heavily on PFC recruitment when engaging cognitively demanding reappraisal strategies while PFC-mediated regulation might not be as task-specific for younger adults. Older adults’ greater reliance on cognitive control processing during emotion regulation may also be reflected in the time needed to implement these strategies.

  5. Cognitive specialization for verbal vs. spatial ability in men and women : Neural and behavioral correlates

    NARCIS (Netherlands)

    Yeo, Ronald A.; Ryman, Sephira G.; Thompson, Melissa E.; van den Heuvel, Martijn P.; de Reus, Marcel A.; Pommy, Jessica; Seaman, Brandi; Jung, Rex E.

    2016-01-01

    An important dimension of individual differences, independent of general cognitive ability (GCA), is specialization for verbal or spatial ability. In this study we investigated neuroanatomic, network, and personality features associated with verbal vs. spatial ability. Healthy young adults (N = 244)

  6. Dreaming Machines : On multimodal fusion and information retrieval using neural-symbolic cognitive agents

    NARCIS (Netherlands)

    Penning, H.L.H. de; Avila Garcez, A. d; Meyer, J.J.C.

    2013-01-01

    Deep Boltzmann Machines (DBM) have been used as a computational cognitive model in various AI-related research and applications, notably in computational vision and multimodal fusion. Being regarded as a biological plausible model of the human brain, the DBM is also becoming a popular instrument to

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Cognitive-motor dual-task interference: A systematic review of neural correlates.

    Science.gov (United States)

    Leone, Carmela; Feys, Peter; Moumdjian, Lousin; D'Amico, Emanuele; Zappia, Mario; Patti, Francesco

    2017-04-01

    Cognitive-motor interference refers to dual-tasking (DT) interference (DTi) occurring when the simultaneous performance of a cognitive and a motor task leads to a percentage change in one or both tasks. Several theories exist to explain DTi in humans: the capacity-sharing, the bottleneck and the cross-talk theories. Numerous studies investigating whether a specific brain locus is associated with cognitive-motor DTi have been conducted, but not systematically reviewed. We aimed to review the evidences on brain activity associated with the cognitive-motor DT, in order to better understand the neurological basis of the CMi. Results were reported according to the technique used to assess brain activity. Twenty-three articles met the inclusion criteria. Out of them, nine studies used functional magnetic resonance imaging to show an additive, under-additive, over- additive, or a mixed activation pattern of the brain. Seven studies used near-infrared spectroscopy, and seven neurophysiological instruments. Yet a specific DT locus in the brain cannot be concluded from the overall current literature. Future studies are warranted to overcome the shortcomings identified. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. The adolescent brain : unraveling the neural mechanisms of cognitive and affective development

    NARCIS (Netherlands)

    Peters, Sabine

    2016-01-01

    Adolescence is often characterized as a period of increased risk taking and impulsive behavior. Researchers have constructed brain-based models to explain the higher prevalence of risk taking during adolescence. It has been hypothesized that brain regions for cognitive control develop relatively

  10. Location-based resource allocation for OFDMA cognitive radio systems

    KAUST Repository

    Nam, Haewoon

    2010-01-01

    In cognitive radio systems, in order for the secondary users to opportunistically share the spectrum without interfering the primary users, an accurate spectrum measurement and a precise estimation of the interference at the primary users are necessary but are challenging tasks. Since it is impractical in cognitive radio systems to assume that the channel state information of the interference link is available at the cognitive transmitter, the interference at the primary users is hard to be estimated accurately. This paper introduces a resource allocation algorithm for OFDMA-based cognitive radio systems, which utilizes location information of the primary and secondary users instead of the channel state information of the interference link. Simulation results show that it is indeed effective to incorporate location information into resource allocation so that a near-optimal capacity is achieved.

  11. Effects of cognitive load on neural and behavioral responses to smoking cue distractors

    Science.gov (United States)

    MacLean, R. Ross; Nichols, Travis T.; LeBreton, James M.; Wilson, Stephen J.

    2017-01-01

    Smoking cessation failures are frequently thought to reflect poor top-down regulatory control over behavior. Previous studies suggest that smoking cues occupy limited working memory resources, an effect that may contribute to difficulty achieving abstinence. Few studies have evaluated the effects of cognitive load on the ability to actively maintain information in the face of distracting smoking cues. The current study adapted an fMRI probed recall task under low and high cognitive load with three distractor conditions: control, neutral images, or smoking-related images. Consistent with a limited-resource model of cue reactivity, we predicted that performance of daily smokers (n=17) would be most impaired when high load was paired with smoking distractors. Results demonstrated a main effect of load, with decreased accuracy under high, compared to low, cognitive load. Surprisingly, an interaction revealed the effect of load was weakest in the smoking cue distractor condition. Along with this behavioral effect, we observed significantly greater activation of the right inferior frontal gyrus (rIFG) in the low load condition relative to the high load condition for trials containing smoking cue distractors. Furthermore, load-related changes in rIFG activation partially mediated the effects of load on task accuracy in the smoking cue distractor condition. These findings are discussed in the context of prevailing cognitive and cue reactivity theories. Results suggest that high cognitive load does not necessarily make smokers more susceptible to interference from smoking-related stimuli, and that elevated load may even have a buffering effect in the presence of smoking cues under certain conditions. PMID:27012714

  12. Autism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition.

    Directory of Open Access Journals (Sweden)

    Gisela Sugranyes

    Full Text Available CONTEXT: Impaired social cognition is a cardinal feature of Autism Spectrum Disorders (ASD and Schizophrenia (SZ. However, the functional neuroanatomy of social cognition in either disorder remains unclear due to variability in primary literature. Additionally, it is not known whether deficits in ASD and SZ arise from similar or disease-specific disruption of the social cognition network. OBJECTIVE: To identify regions most robustly implicated in social cognition processing in SZ and ASD. DATA SOURCES: Systematic review of English language articles using MEDLINE (1995-2010 and reference lists. STUDY SELECTION: Studies were required to use fMRI to compare ASD or SZ subjects to a matched healthy control group, provide coordinates in standard stereotactic space, and employ standardized facial emotion recognition (FER or theory of mind (TOM paradigms. DATA EXTRACTION: Activation foci from studies meeting inclusion criteria (n = 33 were subjected to a quantitative voxel-based meta-analysis using activation likelihood estimation, and encompassed 146 subjects with ASD, 336 SZ patients and 492 healthy controls. RESULTS: Both SZ and ASD showed medial prefrontal hypoactivation, which was more pronounced in ASD, while ventrolateral prefrontal dysfunction was associated mostly with SZ. Amygdala hypoactivation was observed in SZ patients during FER and in ASD during more complex ToM tasks. Both disorders were associated with hypoactivation within the Superior Temporal Sulcus (STS during ToM tasks, but activation in these regions was increased in ASD during affect processing. Disease-specific differences were noted in somatosensory engagement, which was increased in SZ and decreased in ASD. Reduced thalamic activation was uniquely seen in SZ. CONCLUSIONS: Reduced frontolimbic and STS engagement emerged as a shared feature of social cognition deficits in SZ and ASD. However, there were disease- and stimulus-specific differences. These findings may aid future

  13. Effects of cognitive load on neural and behavioral responses to smoking-cue distractors.

    Science.gov (United States)

    MacLean, R Ross; Nichols, Travis T; LeBreton, James M; Wilson, Stephen J

    2016-08-01

    Smoking cessation failures are frequently thought to reflect poor top-down regulatory control over behavior. Previous studies have suggested that smoking cues occupy limited working memory resources, an effect that may contribute to difficulty achieving abstinence. Few studies have evaluated the effects of cognitive load on the ability to actively maintain information in the face of distracting smoking cues. For the present study, we adapted an fMRI probed recall task under low and high cognitive load with three distractor conditions: control, neutral images, or smoking-related images. Consistent with a limited-resource model of cue reactivity, we predicted that the performance of daily smokers (n = 17) would be most impaired when high load was paired with smoking distractors. The results demonstrated a main effect of load, with decreased accuracy under high, as compared to low, cognitive load. Surprisingly, an interaction revealed that the effect of load was weakest in the smoking cue distractor condition. Along with this behavioral effect, we observed significantly greater activation of the right inferior frontal gyrus (rIFG) in the low-load condition than in the high-load condition for trials containing smoking cue distractors. Furthermore, load-related changes in rIFG activation partially mediated the effects of load on task accuracy in the smoking-cue distractor condition. These findings are discussed in the context of prevailing cognitive and cue reactivity theories. These results suggest that high cognitive load does not necessarily make smokers more susceptible to interference from smoking-related stimuli, and that elevated load may even have a buffering effect in the presence of smoking cues under certain conditions.

  14. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  15. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  16. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  17. How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network

    Directory of Open Access Journals (Sweden)

    Holk eCruse

    2013-06-01

    Full Text Available An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture to control non-trivial hexapod walking in unpredictable environment (Walknet as well as insect-like navigation (Navinet. In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and - via internal simulation - of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we focus on the question to what extent properties of phenomena to be characterized on a different level of description as for example consciousness can be found in this minimally cognitive system. Adopting a monist view, we argue that the phenomenal aspect of mental phenomena can be neglected when discussing the function of such a system. Under this condition, reaCog is discussed to be equipped with properties as are bottom-up and top-down attention, intentions, volition and some aspects of Access Consciousness. These properties have not been explicitly implemented but emerge from the cooperation between the elements of the network. The aspects of access consciousness found in reaCog concern the above mentioned ability to plan ahead and to invent and guide (new actions. Furthermore, global accessibility of memory elements, another aspect characterizing Access Consciousness is realized by this network. reaCog allows for both reactive/automatic control and (access- conscious control of behavior. We discuss examples for interactions between both the reactive domain and the conscious domain. Metacognition or Reflexive Consciousness is not a property of reaCog. Possible expansions are discussed to allow for further properties of Access Consciousness, verbal report on internal states, and for Metacognition. In summary, we argue that already simple networks allow for properties of consciousness if leaving the phenomenal

  18. How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network.

    Science.gov (United States)

    Cruse, Holk; Schilling, Malte

    2013-01-01

    An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture developed to control non-trivial hexapod walking in an unpredictable environment (Walknet) while using insect-like navigation (Navinet). In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and - via internal simulation - of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we focus on the question to what extent properties of phenomena to be characterized on a different level of description as for example consciousness can be found in this minimally cognitive system. Adopting a monist view, we argue that the phenomenal aspect of mental phenomena can be neglected when discussing the function of such a system. Under this condition, reaCog is discussed to be equipped with properties as are bottom-up and top-down attention, intentions, volition, and some aspects of Access Consciousness. These properties have not been explicitly implemented but emerge from the cooperation between the elements of the network. The aspects of Access Consciousness found in reaCog concern the above mentioned ability to plan ahead and to invent and guide (new) actions. Furthermore, global accessibility of memory elements, another aspect characterizing Access Consciousness is realized by this network. reaCog allows for both reactive/automatic control and (access-) conscious control of behavior. We discuss examples for interactions between both the reactive domain and the conscious domain. Metacognition or Reflexive Consciousness is not a property of reaCog. Possible expansions are discussed to allow for further properties of Access Consciousness, verbal report on internal states, and for Metacognition. In summary, we argue that already simple networks allow for properties of consciousness if leaving the phenomenal

  19. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  20. From neuro-pigments to neural efficiency: The relationship between retinal carotenoids and behavioral and neuroelectric indices of cognitive control in childhood.

    Science.gov (United States)

    Walk, Anne M; Khan, Naiman A; Barnett, Sasha M; Raine, Lauren B; Kramer, Arthur F; Cohen, Neal J; Moulton, Christopher J; Renzi-Hammond, Lisa M; Hammond, Billy R; Hillman, Charles H

    2017-08-01

    Lutein and zeaxanthin are plant pigments known to preferentially accumulate in neural tissue. Macular Pigment Optical Density (MPOD), a non-invasive measure of retinal carotenoids and surrogate measure of brain carotenoid concentration, has been associated with disease prevention and cognitive health. Superior MPOD status in later adulthood has been shown to provide neuroprotective effects on cognition. Given that childhood signifies a critical period for carotenoid accumulation in brain, it is likely that the beneficial impact would be evident during development, though this relationship has not been directly investigated. The present study investigated the relationship between MPOD and the behavioral and neuroelectric indices elicited during a cognitive control task in preadolescent children. 49 participants completed a modified flanker task while event-related potentials (ERPs) were recorded to assess the P3 component of the ERP waveform. MPOD was associated with both behavioral performance and P3 amplitude such that children with higher MPOD had more accurate performance and lower P3 amplitudes. These relationships were more pronounced for trials requiring greater amounts of cognitive control. These results indicate that children with higher MPOD may respond to cognitive tasks more efficiently, maintaining high performance while displaying neural indices indicative of lower cognitive load. These findings provide novel support for the neuroprotective influence of retinal carotenoids during preadolescence. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  2. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  3. The neural basis of rationalization: cognitive dissonance reduction during decision-making.

    Science.gov (United States)

    Jarcho, Johanna M; Berkman, Elliot T; Lieberman, Matthew D

    2011-09-01

    People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.

  4. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  5. Flood forecasting within urban drainage systems using NARX neural network.

    Science.gov (United States)

    Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi

    2017-11-01

    Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

  6. Investigating the Neural Bases for Intra-Subject Cognitive Efficiency Using Functional Magnetic Resonance Imaging

    Directory of Open Access Journals (Sweden)

    Neena K. Rao

    2014-10-01

    Full Text Available Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT, were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-related patterns, or RT-BOLD correlations where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals.

  7. Neural correlates of spatial navigation changes in mild cognitive impairment and Alzheimer's disease

    Czech Academy of Sciences Publication Activity Database

    Vlček, Kamil; Laczó, J.

    2014-01-01

    Roč. 8, Mar 17 (2014), s. 89 ISSN 1662-5153 R&D Projects: GA MZd(CZ) NT13386 Grant - others:GA MŠk(CZ) ED1.100/02/0123 Institutional support: RVO:67985823 Keywords : spatial navigation * Alzheimer’s disease * spatial disorientation * brain changes * mild cognitive impairment Subject RIV: FH - Neurology Impact factor: 3.270, year: 2014

  8. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  9. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  10. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  11. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  12. Companion Cognitive Systems: A Step toward Human-Level AI

    OpenAIRE

    Forbus, Kenneth D.; Hinrichs, Thomas R.

    2006-01-01

    We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and...

  13. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring.

    Science.gov (United States)

    Supekar, Kaustubh; Iuculano, Teresa; Chen, Lang; Menon, Vinod

    2015-09-09

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. Significance statement: Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  14. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  15. Individual differences in emotion-cognition interactions: Emotional valence interacts with serotonin transporter genotype to influence brain systems involved in emotional reactivity and cognitive control

    Directory of Open Access Journals (Sweden)

    Melanie eStollstorff

    2013-07-01

    Full Text Available The serotonin transporter gene (5-HTTLPR influences emotional reactivity and attentional bias towards or away from emotional stimuli and has been implicated in psychopathological states, such as depression and anxiety disorder. The short allele is associated with increased reactivity and attention towards negatively-valenced emotional information, whereas the long allele is associated with that towards positively-valenced emotional information. The neural basis for individual differences in the ability to exert cognitive control over these bottom-up biases in emotional reactivity and attention is unknown, an issue investigated in the present study. Two groups, homozygous 5-HTTLPR long allele carriers or homozygous short allele carriers, underwent functional magnetic resonance imaging (fMRI while completing an Emotional Stroop-like task that varied with regards to the congruency of task-relevant and task-irrelevant information and the emotional valence of the task-irrelevant information. Behaviorally, participants demonstrated the classic Stroop effect (slower responses for incongruent than congruent trials, which did not differ by 5-HTTLPR genotype. However, fMRI results revealed that genotype influenced the degree to which neural systems were engaged depending on the valence of the conflicting task-irrelevant information. While the Long group recruited prefrontal control regions and superior temporal sulcus during conflict when task-irrelevant information was positively-valenced, the "Short" group recruited these regions when task-irrelevant information was negatively-valenced. Thus, participants successfully engaged cognitive control to overcome conflict in an emotional context using similar neural circuitry, but the engagement of this circuitry depended on emotional valence and 5-HTTLPR status. These results suggest that the interplay between emotion and cognition is modulated, in part, by a genetic polymorphism that influences serotonin

  16. Cooperative Cognitive Radio Systems over Nakagami-m Fading Channels

    KAUST Repository

    Hyadi, Amal

    2013-05-08

    This thesis aims to investigate the incorporation of cooperative techniques in cognitive radio networks over Nakagami-m fading channels. These last years, spectrum sharing mechanisms has gained a lot of interest in the wireless communication domain. Using cooperation in a cognitive set up make the use of spectrum much more efficient. Moreover, it helps to extend the coverage area of the cognitive network and also to reduce the transmitting power and, thus, the generated interference. In this work, we consider two particular scenarios for cooperative cognitive radio systems. The first scenario consider multihop regenerative relaying in an underlay cognitive set up. The cooperation is performed in the secondary system, in the presence of multiple primary users. Both interference power and peak power constraints are taking into account. Closed-form expressions for the statistical characteristics and multiple end- to-end performance metrics are derived. Different scenarios are presented to illustrate the obtained results and Monte Carlo simulations confirm the accuracy of our analytical derivations. In the second part of this work, we consider an overlay cognitive network with the spectrally efficient two-phase two-way relaying protocol. Two relay selection techniques, optimizing both the primary and the secondary communication, are presented. The overall outage performance is investigated and an optimal power allocation scheme, that ameliorate the outage performance of the system, is proposed. Numerical simulations are presented to illustrate and compare the obtained results.

  17. BRAIN Journal - Cognitive Medical Multiagent Systems

    OpenAIRE

    Barna Iantovics

    2017-01-01

    Abstract The development of efficient and flexible agent-based medical diagnosis systems represents a recent research direction. Medical multiagent systems may improve the efficiency of traditionally developed medical computational systems, like the medical expert systems. In our previous researches, a novel cooperative medical diagnosis multiagent system called CMDS (Contract Net Based Medical Diagnosis System) was proposed. CMDS system can solve flexibly a large variety of medical diagn...

  18. Dynamical system with plastic self-organized velocity field as an alternative conceptual model of a cognitive system.

    Science.gov (United States)

    Janson, Natalia B; Marsden, Christopher J

    2017-12-05

    It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type.

  19. Linking perception, cognition, and action: psychophysical observations and neural network modelling.

    Directory of Open Access Journals (Sweden)

    Juan Carlos Méndez

    Full Text Available It has been argued that perception, decision making, and movement planning are in reality tightly interwoven brain processes. However, how they are implemented in neural circuits is still a matter of debate. We tested human subjects in a temporal categorization task in which intervals had to be categorized as short or long. Subjects communicated their decision by moving a cursor into one of two possible targets, which appeared separated by different angles from trial to trial. Even though there was a 1 second-long delay between interval presentation and decision communication, categorization difficulty affected subjects' performance, reaction (RT and movement time (MT. In addition, reaction and movement times were also influenced by the distance between the targets. This implies that not only perceptual, but also movement-related considerations were incorporated into the decision process. Therefore, we searched for a model that could use categorization difficulty and target separation to describe subjects' performance, RT, and MT. We developed a network consisting of two mutually inhibiting neural populations, each tuned to one of the possible categories and composed of an accumulation and a memory node. This network sequentially acquired interval information, maintained it in working memory and was then attracted to one of two possible states, corresponding to a categorical decision. It faithfully replicated subjects' RT and MT as a function of categorization difficulty and target distance; it also replicated performance as a function of categorization difficulty. Furthermore, this model was used to make new predictions about the effect of untested durations, target distances and delay durations. To our knowledge, this is the first biologically plausible model that has been proposed to account for decision making and communication by integrating both sensory and motor planning information.

  20. Transdiagnostic neural markers of emotion-cognition interaction in psychotic disorders.

    Science.gov (United States)

    Sabharwal, Amri; Szekely, Akos; Kotov, Roman; Mukherjee, Prerona; Leung, Hoi-Chung; Barch, Deanna M; Mohanty, Aprajita

    2016-10-01

    Deficits in working memory (WM) and emotion processing are prominent impairments in psychotic disorders, and have been linked to reduced quality of life and real-world functioning. Translation of knowledge regarding the neural circuitry implementing these deficits into improved diagnosis and targeted treatments has been slow, possibly because of categorical definitions of disorders. Using the dimensional Research Domain Criteria (RDoC) framework, we investigated the clinical and practical utility of transdiagnostic behavioral and neural measures of emotion-related WM disruption across psychotic disorders. Behavioral and functional MRI data were recorded while 53 participants with psychotic disorders and 29 participants with no history of psychosis performed a modified n-back task with fear and neutral distractors. Hierarchical regression analyses showed that psychotic symptoms entered after diagnosis accounted for unique variance in fear versus neutral accuracy and activation in the ventrolateral, dorsolateral, and dorsomedial prefrontal cortex, but diagnostic group entered after psychotic symptoms did not. These results remained even after controlling for negative symptoms, disorganized symptoms, and dysphoria. Finally, worse accuracy and greater prefrontal activity were associated with poorer social functioning and unemployment across diagnostic groups. Present results support the transdiagnostic nature of behavioral and neuroimaging measures of emotion-related WM disruption as they relate to psychotic symptoms, irrespective of diagnosis. They also provide support for the practical utility of these markers in explaining real-world functioning. Overall, these results elucidate key aspects of the RDoC construct of WM maintenance by clarifying its transdiagnostic importance and clinical utility in psychotic disorders. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

    Science.gov (United States)

    Gao, Rong

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

  2. Cognitive, psychophysical, and neural correlates of vulvar pain in primary and secondary provoked vestibulodynia: a pilot study.

    Science.gov (United States)

    Sutton, Katherine; Pukall, Caroline; Wild, Conor; Johnsrude, Ingrid; Chamberlain, Susan

    2015-05-01

    Provoked vestibulodynia (PVD) is a common condition characterized by localized, provoked pain that can be present since first vaginal penetration attempt (primary) or can develop after a period of pain-free penetration (secondary). Research has demonstrated psychosocial and psychophysical differences between women with these subtypes of PVD, but the question of whether neural responses to pain also differ remains to be investigated. This study aims to examine whether cognitive, psychophysical, and neural responses to vulvar pressure pain differ between women with PVD1 and PVD2. Women with PVD1 and PVD2 were compared for group differences using multiple modalities, including questionnaires, psychophysical testing, and neuroimaging. Pain ratings were held constant across groups, rather than amount of pressure applied. Demographics, sexual functioning, four questionnaires examining anxiety and catastrophizing, quantitative sensory testing at the vulvar vestibule using a vulvalgesiometer, and functional and structural magnetic resonance imaging (MRI). Findings suggest that women with PVD1 are more anxious and that they catastrophize more about their vulvar and nonvulvar pain than women with PVD2. Overall, MRI results demonstrated structural and functional similarities to other chronic pain findings for both groups of women. Gray matter (GM) density also differed between groups: women with PVD1 showed significant decreases in GM throughout areas associated with pain processing. Functionally, between-groups differences were found during painful vulvar stimulation despite lower pressures applied to the vulva for women with PVD1 because of their heightened sensitivity; the determination of the level of vulvar pressure to elicit pain was based on subjective ratings. Findings are limited by sample size and liberal alpha values; however, future research is certainly warranted based on the preliminary findings of this study suggesting both similarities and differences between

  3. Notch1 deficiency in postnatal neural progenitor cells in the dentate gyrus leads to emotional and cognitive impairment.

    Science.gov (United States)

    Feng, Shufang; Shi, Tianyao; Qiu, Jiangxia; Yang, Haihong; Wu, Yan; Zhou, Wenxia; Wang, Wei; Wu, Haitao

    2017-10-01

    It is well known that Notch1 signaling plays a crucial role in embryonic neural development and adult neurogenesis. The latest evidence shows that Notch1 also plays a critical role in synaptic plasticity in mature hippocampal neurons. So far, deeper insights into the function of Notch1 signaling during the different steps of adult neurogenesis are still lacking, and the mechanisms by which Notch1 dysfunction is associated with brain disorders are also poorly understood. In the current study, we found that Notch1 was highly expressed in the adult-born immature neurons in the hippocampal dentate gyrus. Using a genetic approach to selectively ablate Notch1 signaling in late immature precursors in the postnatal hippocampus by cross-breeding doublecortin (DCX) + neuron-specific proopiomelanocortin (POMC)-α Cre mice with floxed Notch1 mice, we demonstrated a previously unreported pivotal role of Notch1 signaling in survival and function of adult newborn neurons in the dentate gyrus. Moreover, behavioral and functional studies demonstrated that POMC-Notch1 -/- mutant mice showed anxiety and depressive-like behavior with impaired synaptic transmission properties in the dentate gyrus. Finally, our mechanistic study showed significantly compromised phosphorylation of cAMP response element-binding protein (CREB) in Notch1 mutants, suggesting that the dysfunction of Notch1 mutants is associated with the disrupted pCREB signaling in postnatally generated immature neurons in the dentate gyrus.-Feng, S., Shi, T., Qiu, J., Yang, H., Wu, Y., Zhou, W., Wang, W., Wu, H. Notch1 deficiency in postnatal neural progenitor cells in the dentate gyrus leads to emotional and cognitive impairment. © FASEB.

  4. Cognitive disorder and changes in cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury

    Institute of Scientific and Technical Information of China (English)

    Weiliang Zhao; Dezhi Kang; Yuanxiang Lin

    2008-01-01

    BACKGROUND: Learning and memory damage is one of the most permanent and the severest symptoms of traumatic brain injury; it can seriously influence the normal life and work of patients. Some research has demonstrated that cognitive disorder is closely related to nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor. OBJECTIVE: To summarize the cognitive disorder and changes in nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury. RETRIEVAL STRATEGY: A computer-based online search was conducted in PUBMED for English language publications containing the key words "brain injured, cognitive handicap, acetylcholine, N-methyl-D aspartate receptors, neural cell adhesion molecule, brain-derived neurotrophic factor" from January 2000 to December 2007. There were 44 papers in total. Inclusion criteria: ① articles about changes in nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury; ② articles in the same researching circle published in authoritative journals or recently published. Exclusion criteria: duplicated articles.LITERATURE EVALUATION: References were mainly derived from research on changes in these four factors following brain injury. The 20 included papers were clinical or basic experimental studies. DATA SYNTHESIS: After craniocerebral injury, changes in these four factors in brain were similar to those during recovery from cognitive disorder, to a certain degree. Some data have indicated that activation of nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor could greatly improve cognitive disorder following brain injury. However, there are still a lot of questions remaining; for example, how do these

  5. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  6. A new evolutionary system for evolving artificial neural networks.

    Science.gov (United States)

    Yao, X; Liu, Y

    1997-01-01

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

  7. Neural underpinnings of background acoustic noise in normal aging and mild cognitive impairment.

    Science.gov (United States)

    Sinanaj, Indrit; Montandon, Marie-Louise; Rodriguez, Cristelle; Herrmann, François; Santini, Francesco; Haller, Sven; Giannakopoulos, Panteleimon

    2015-12-03

    Previous contributions in younger cohorts have revealed that reallocation of cerebral resources, a crucial mechanism for working memory (WM), may be disrupted by parallel demands of background acoustic noise suppression. To date, no study has explored the impact of such disruption on brain activation in elderly individuals with or without subtle cognitive deficits. We performed a functional Magnetic Resonance Imaging (fMRI) study in 23 cases (mean age=75.7 y.o., 16 men) with mild cognitive impairment (MCI) and 16 elderly healthy controls (HC, mean age=70.1 y.o., three men) using a 2-back WM task, under two distinct MRI background acoustic noise conditions (louder vs. lower noise echo-planar imaging). General linear models were used to assess brain activation as a function of group and noise. In both groups, lower background noise is associated with increased activation of the working memory network (WMN). A decrease of the normally observed deactivation of the default mode network (DMN) is found under louder noise in both groups. Unlike HC, MCI cases also show decreased deactivation of the DMN under both louder and lower background noise. Under louder noise, this decrease is observed in anterior parts of the DMN in HC, and in the posterior cingulate cortex in MCI cases. Our results suggest that background acoustic noise has a differential impact on WMN activation in normal aging as a function of the cognitive status. Only louder noise has a disruptive effect on the usually observed DMN deactivation during WM task performance in HC. In contrast, MCI cases show altered DMN reactivity even in the presence of lower noise. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. Dual PECCS: a cognitive system for conceptual representation and categorization

    Science.gov (United States)

    Lieto, Antonio; Radicioni, Daniele P.; Rho, Valentina

    2017-03-01

    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and - in addition - that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual- PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.

  9. Supervised cognitive system: A new vision for cognitive engine design in wireless networks

    KAUST Repository

    Alqerm, Ismail

    2018-03-19

    Cognitive radio attracts researchers\\' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system parameters\\' adaptation using optimization and machine learning techniques. However, these techniques have strengths and weaknesses depending on the experienced network scenario that make one more appropriate than others. In this paper, we propose a novel design for the cognitive system called supervised cognitive system (SCS), which aims to perform radio parameters adaptation with the most appropriate CE learning technique for the encountered network scenario. To realize SCS, it is required to evaluate the performance of different CEs in different network scenarios and according to certain performance objectives. In addition, the ability to select the most appropriate CE learning technique for adaptation in the current network scenario is also a priority in our design. Therefore, SCS investigates the relationship between learning and performance improvement and it employs online learning to classify scenarios and select the most appropriate CE learning technique. The testbed implementation and evaluation results in terms of goodput, packet error rate, and spectral efficiency show that the proposed SCS achieves more than 50% in performance gain compared to the best standalone CE.

  10. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  11. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  12. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

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

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

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

  14. Neural substrates of motor and cognitive dysfunctions in SCA2 patients: A network based statistics analysis

    Directory of Open Access Journals (Sweden)

    G. Olivito

    2017-01-01

    In the present study, the network-based statistics (NBS approach was used to assess differences in functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes.

  15. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  16. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  17. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  18. Cognitive Process as a Basis for Intelligent Retrieval Systems Design.

    Science.gov (United States)

    Chen, Hsinchun; Dhar, Vasant

    1991-01-01

    Two studies of the cognitive processes involved in online document-based information retrieval were conducted. These studies led to the development of five computational models of online document retrieval which were incorporated into the design of an "intelligent" document-based retrieval system. Both the system and the broader implications of…

  19. Generalized location-based resource allocation for OFDMA cognitive radio systems

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2010-01-01

    Cognitive radio is one of the hot topics for emerging and future wireless communication. Cognitive users can share channels with primary users under the condition of non interference. In order to compute this interference, the cognitive system

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

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

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

  1. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)