Neural implementation of musical expertise and cognitive transfers: Could they be promising in the framework of normal cognitive aging?

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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.

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

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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.

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

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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.

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

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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

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

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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.

Infant joint attention, neural networks and social cognition.

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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.

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

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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.

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

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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

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

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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.

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

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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.

Neurally-dissociable cognitive components of reading deficits in subacute stroke

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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

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

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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.

Acquiring neural signals for developing a perception and cognition model

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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.

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

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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.

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

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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.

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

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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

Neurally dissociable cognitive components of reading deficits in subacute stroke.

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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.

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.

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

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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.

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

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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.

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

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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.

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

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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.

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.

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

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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.

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

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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.

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

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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.

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...

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

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

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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.

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

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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.

A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

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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.

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

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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

Evolvable synthetic neural system

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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.

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

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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.

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

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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.

Neuromorphic cognitive systems a learning and memory centered approach

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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...

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

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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.

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

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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.

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

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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,…

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

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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.

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

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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

Behavioral, Cognitive and Neural Markers of Asperger Syndrome

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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. 

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

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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.

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

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

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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.

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.

Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

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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.

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

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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.

Neural dynamics of the cognitive map in the hippocampus.

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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

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

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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).

A Neural Information Field Approach to Computational Cognition

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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

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

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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).…

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.

Neural Cognition and Affective Computing on Cyber Language.

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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.

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.

Anticipatory Cognitive Systems: a Theoretical Model

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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.

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

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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.

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...

Cognitive abnormalities and neural mechanisms in post-traumatic stress disorder

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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

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

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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.

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

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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.

Cognition, emotion, and attention.

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Müller-Oehring, Eva M; Schulte, Tilman

2014-01-01

Deficits of attention, emotion, and cognition occur in individuals with alcohol abuse and addiction. This review elucidates the concepts of attention, emotion, and cognition and references research on the underlying neural networks and their compromise in alcohol use disorder. Neuroimaging research on adolescents with family history of alcoholism contributes to the understanding of pre-existing brain structural conditions and characterization of cognition and attention processes in high-risk individuals. Attention and cognition interact with other brain functions, including perceptual selection, salience, emotion, reward, and memory, through interconnected neural networks. Recent research reports compromised microstructural and functional network connectivity in alcoholism, which can have an effect on the dynamic tuning between brain systems, e.g., the frontally based executive control system, the limbic emotion system, and the midbrain-striatal reward system, thereby impeding cognitive flexibility and behavioral adaptation to changing environments. Finally, we introduce concepts of functional compensation, the capacity to generate attentional resources for performance enhancement, and brain structure recovery with abstinence. An understanding of the neural mechanisms of attention, emotion, and cognition will likely provide the basis for better treatment strategies for developing skills that enhance alcoholism therapy adherence and quality of life, and reduce the propensity for relapse. © 2014 Elsevier B.V. All rights reserved.

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

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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.

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

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.

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

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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.

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 ...

Neural plasticity of development and learning.

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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.

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

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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.

The influence of combined cognitive plus social-cognitive training on amygdala response during face emotion recognition in schizophrenia.

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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.

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

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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…

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

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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.

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

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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

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

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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.

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

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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.

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

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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.

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

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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

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

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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.

Cognitive and Neural Correlates of Aging in Autism Spectrum Disorder

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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

False memories with age: Neural and cognitive underpinnings.

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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.

False memories with age: neural and cognitive underpinnings

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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

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

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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.

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

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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

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....

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.

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

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...

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

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

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Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

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

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

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.

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...

A Systematic Review for Functional Neuroimaging Studies of Cognitive Reserve Across the Cognitive Aging Spectrum.

Science.gov (United States)

Anthony, Mia; Lin, Feng

2017-12-13

Cognitive reserve has been proposed to explain the discrepancy between clinical symptoms and the effects of aging or Alzheimer's pathology. Functional magnetic resonance imaging (fMRI) may help elucidate how neural reserve and compensation delay cognitive decline and identify brain regions associated with cognitive reserve. This systematic review evaluated neural correlates of cognitive reserve via fMRI (resting-state and task-related) studies across the cognitive aging spectrum (i.e., normal cognition, mild cognitive impairment, and Alzheimer's disease). This review examined published articles up to March 2017. There were 13 cross-sectional observational studies that met the inclusion criteria, including relevance to cognitive reserve, subjects 60 years or older with normal cognition, mild cognitive impairment, and/or Alzheimer's disease, at least one quantitative measure of cognitive reserve, and fMRI as the imaging modality. Quality assessment of included studies was conducted using the Newcastle-Ottawa Scale adapted for cross-sectional studies. Across the cognitive aging spectrum, medial temporal regions and an anterior or posterior cingulate cortex-seeded default mode network were associated with neural reserve. Frontal regions and the dorsal attentional network were related to neural compensation. Compared to neural reserve, neural compensation was more common in mild cognitive impairment and Alzheimer's disease. Neural reserve and compensation both support cognitive reserve, with compensation more common in later stages of the cognitive aging spectrum. Longitudinal and intervention studies are needed to investigate changes between neural reserve and compensation during the transition between clinical stages, and to explore the causal relationship between cognitive reserve and potential neural substrates. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Nonlinear Dynamics of Emotion-Cognition Interaction: When Emotion Does not Destroy Cognition?

OpenAIRE

Afraimovich, Valentin; Young, Todd; Muezzinoglu, Mehmet K.; Rabinovich, Mikhail I.

2010-01-01

Emotion (i.e., spontaneous motivation and subsequent implementation of a behavior) and cognition (i.e., problem solving by information processing) are essential to how we, as humans, respond to changes in our environment. Recent studies in cognitive science suggest that emotion and cognition are subserved by different, although heavily integrated, neural systems. Understanding the time-varying relationship of emotion and cognition is a challenging goal with important implications for neurosci...

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

Embedding reward signals into perception and cognition

Directory of Open Access Journals (Sweden)

Luiz Pessoa

2010-09-01

Full Text Available Despite considerable interest in the neural basis of valuation, how valuation affects cognitive processing has received relatively less attention. Here, we review evidence from recent behavioral and neuroimaging studies supporting the notion that motivation can enhance perceptual and executive control processes to achieve more efficient goal-directed behavior. Specifically, in the context of cognitive tasks offering monetary gains, improved behavioral performance has been repeatedly observed in conjunction with elevated neural activations in task-relevant perceptual, cognitive, and reward-related regions. We address the neural basis of motivation-cognition interactions by suggesting various modes of communication between relevant neural networks: (1 global hub regions may integrate information from multiple inputs providing a communicative link between specialized networks, (2 point-to-point interactions allow for more specific cross-network communication, and (3 diffuse neuromodulatory systems can relay motivational signals to cortex and enhance signal processing. Together, these modes of communication allow information regarding motivational significance to reach relevant brain regions and shape behavior.

Neural Systems Laboratory

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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...

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

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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.

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.

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.

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

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.

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.

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.

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...

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.

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.

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

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.

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.

Development of realtime cognitive state estimator

International Nuclear Information System (INIS)

Takahashi, Makoto; Kitamura, Masashi; Yoshikaea, Hidekazu

2004-01-01

The realtime cognitive state estimator based on the set of physiological measures has been developed in order to provide valuable information on the human behavior during the interaction through the Man-Machine Interface. The artificial neural network has been adopted to categorize the cognitive states by using the qualitative physiological data pattern as the inputs. The laboratory experiments, in which the subjects' cognitive states were intentionally controlled by the task presented, were performed to obtain training data sets for the neural network. The developed system has been shown to be capable of estimating cognitive state with higher accuracy and realtime estimation capability has also been confirmed through the data processing experiments. (author)

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.

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.

Cognitive memory.

Science.gov (United States)

Widrow, Bernard; Aragon, Juan Carlos

2013-05-01

Regarding the workings of the human mind, memory and pattern recognition seem to be intertwined. You generally do not have one without the other. Taking inspiration from life experience, a new form of computer memory has been devised. Certain conjectures about human memory are keys to the central idea. The design of a practical and useful "cognitive" memory system is contemplated, a memory system that may also serve as a model for many aspects of human memory. The new memory does not function like a computer memory where specific data is stored in specific numbered registers and retrieval is done by reading the contents of the specified memory register, or done by matching key words as with a document search. Incoming sensory data would be stored at the next available empty memory location, and indeed could be stored redundantly at several empty locations. The stored sensory data would neither have key words nor would it be located in known or specified memory locations. Sensory inputs concerning a single object or subject are stored together as patterns in a single "file folder" or "memory folder". When the contents of the folder are retrieved, sights, sounds, tactile feel, smell, etc., are obtained all at the same time. Retrieval would be initiated by a query or a prompt signal from a current set of sensory inputs or patterns. A search through the memory would be made to locate stored data that correlates with or relates to the prompt input. The search would be done by a retrieval system whose first stage makes use of autoassociative artificial neural networks and whose second stage relies on exhaustive search. Applications of cognitive memory systems have been made to visual aircraft identification, aircraft navigation, and human facial recognition. Concerning human memory, reasons are given why it is unlikely that long-term memory is stored in the synapses of the brain's neural networks. Reasons are given suggesting that long-term memory is stored in DNA or RNA

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

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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.

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.

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

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.

Neural Computations in a Dynamical System with Multiple Time Scales

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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.

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.

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'.

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

Using Dual Process Models to Examine Impulsivity Throughout Neural Maturation.

Science.gov (United States)

Leshem, Rotem

2016-01-01

The multivariate construct of impulsivity is examined through neural systems and connections that comprise the executive functioning system. It is proposed that cognitive and behavioral components of impulsivity can be divided into two distinct groups, mediated by (1) the cognitive control system: deficits in top-down cognitive control processes referred to as action/cognitive impulsivity and (2) the socioemotional system: related to bottom-up affective/motivational processes referred to as affective impulsivity. Examination of impulsivity from a developmental viewpoint can guide future research, potentially enabling the selection of more effective interventions for impulsive individuals, based on the cognitive components requiring improvement.

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.

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.

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.

Memory and cognitive control circuits in mathematical cognition and learning

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Menon, V.

2018-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

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.

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

Estrogens and Cognition: Friends or Foes?

Science.gov (United States)

Korol, Donna L.; Pisani, Samantha L.

2015-01-01

Estrogens are becoming well known for their robust enhancement on cognition particularly for learning and memory that relies upon functioning of the hippocampus and related neural systems. What is also emerging is that estrogen modulation of cognition is not uniform, at times enhancing yet at other times impairing learning. This review explores the bidirectional effects of estrogens on learning from a multiple memory systems view, focusing on the hippocampus and striatum, whereby modulation by estrogens sorts according to task attributes and neural systems engaged during cognition. We highlight our findings that show the ability to solve hippocampus-sensitive tasks typically improves under relatively high estrogen status while the ability to solve striatum-sensitive tasks degrades with estrogen exposures. Though constrained by dose and timing of exposure, these opposing enhancements and impairments of cognition can be observed following treatments with different estrogenic compounds including the hormone estradiol, the isoflavone genistein found in soybeans, and agonists that are selective for specific estrogen receptors, suggesting that activation of a single receptor type is sufficient to produce the observed shifts in learning strategies. Using this multi-dimensional framework will allow us to extend our thinking of the relationship between estrogens and cognition to other brain regions and cognitive functions. PMID:26149525

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

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

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...

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.

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

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…

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.

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.

Cognition-emotion dysinteraction in schizophrenia.

Science.gov (United States)

Anticevic, Alan; Corlett, Philip R

2012-01-01

Evolving theories of schizophrenia emphasize a "disconnection" in distributed fronto-striatal-limbic neural systems, which may give rise to breakdowns in cognition and emotional function. We discuss these diverse domains of function from the perspective of disrupted neural circuits involved in "cold" cognitive vs. "hot" affective operations and the interplay between these processes. We focus on three research areas that highlight cognition-emotion dysinteractions in schizophrenia: First, we discuss the role of cognitive deficits in the "maintenance" of emotional information. We review recent evidence suggesting that motivational abnormalities in schizophrenia may in part arise due to a disrupted ability to "maintain" affective information over time. Here, dysfunction in a prototypical "cold" cognitive operation may result in "affective" deficits in schizophrenia. Second, we discuss abnormalities in the detection and ascription of salience, manifest as excessive processing of non-emotional stimuli and inappropriate distractibility. We review emerging evidence suggesting deficits in some, but not other, specific emotional processes in schizophrenia - namely an intact ability to perceive emotion "in-the-moment" but poor prospective valuation of stimuli and heightened reactivity to stimuli that ought to be filtered. Third, we discuss abnormalities in learning mechanisms that may give rise to delusions, the fixed, false, and often emotionally charged beliefs that accompany psychosis. We highlight the role of affect in aberrant belief formation, mostly ignored by current theoretical models. Together, we attempt to provide a consilient overview for how breakdowns in neural systems underlying affect and cognition in psychosis interact across symptom domains. We conclude with a brief treatment of the neurobiology of schizophrenia and the need to close our explanatory gap between cellular-level hypotheses and complex behavioral symptoms observed in this illness.

Neural activation in stress-related exhaustion

DEFF Research Database (Denmark)

Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

2017-01-01

The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...... to uphold task performance. Following cognitive training, increased neural activation was observed during 3-back in working memory-related regions, including the striatum, however, low sample size limits any firm conclusions....

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

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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.

Memory and cognitive control circuits in mathematical cognition and learning.

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Menon, V

2016-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

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.

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

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.

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.

Representation of cognitive reappraisal goals in frontal gamma oscillations.

Science.gov (United States)

Kang, Jae-Hwan; Jeong, Ji Woon; Kim, Hyun Taek; Kim, Sang Hee; Kim, Sung-Phil

2014-01-01

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35-55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals

[Cognitive abnormalities and cannabis use].

Science.gov (United States)

Solowij, Nadia; Pesa, Nicole

2010-05-01

Evidence that cannabis use impairs cognitive function in humans has been accumulating in recent decades. The purpose of this overview is to update knowledge in this area with new findings from the most recent literature. Literature searches were conducted using the Web of Science database up to February 2010. The terms searched were: "cannabi*" or "marijuana", and "cogniti*" or "memory" or "attention" or "executive function", and human studies were reviewed preferentially over the animal literature. Cannabis use impairs memory, attention, inhibitory control, executive functions and decision making, both during the period of acute intoxication and beyond, persisting for hours, days, weeks or more after the last use of cannabis. Pharmacological challenge studies in humans are elucidating the nature and neural substrates of cognitive changes associated with various cannabinoids. Long-term or heavy cannabis use appears to result in longer-lasting cognitive abnormalities and possibly structural brain alterations. Greater adverse cognitive effects are associated with cannabis use commencing in early adolescence. The endogenous cannabinoid system is involved in regulatory neural mechanisms that modulate processes underlying a range of cognitive functions that are impaired by cannabis. Deficits in human users most likely therefore reflect neuroadaptations and altered functioning of the endogenous cannabinoid system.

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.

Cognition-Emotion Dysinteraction in Schizophrenia

Directory of Open Access Journals (Sweden)

Alan eAnticevic

2012-10-01

Full Text Available Evolving theories of schizophrenia emphasize a ‘disconnection’ in distributed fronto-striatal-limbic neural systems, which may give rise to breakdowns in cognition and emotional function. We discuss these diverse domains of function from the perspective of disrupted neural circuits involved in ‘cold’ cognitive vs. ‘hot’ affective operations and the interplay between these processes. We focus on three research areas that highlight cognition-emotion dysinteractions in schizophrenia: First, we discuss the role of cognitive deficits in the ‘maintenance’ of emotional information. We review recent evidence suggesting that motivational abnormalities in schizophrenia may in part arise due to a disrupted ability to ‘maintain’ affective information over time. Here, dysfunction in a prototypical ‘cold’ cognitive operation may result in ‘affective’ deficits in schizophrenia. Second, we discuss abnormalities in the detection and ascription of salience, manifest as excessive processing of non-emotional stimuli and inappropriate distractibility. We review emerging evidence suggesting deficits in some, but not other, specific emotional processes in schizophrenia – namely an intact ability to perceive emotion ‘in the moment’ but poor prospective valuation of stimuli and heightened reactivity to stimuli that ought to be filtered. Third, we discuss abnormalities in learning mechanisms that may give rise to delusions, the fixed, false and often emotionally charged beliefs that accompany psychosis. We discuss the role of affect in aberrant belief formation, mostly ignored by current theoretical models. Together, we attempt to provide a consilient overview for how breakdowns in neural systems underlying affect and cognition in psychosis interact across symptom domains. We conclude with a brief treatment of the neurobiology of schizophrenia and the need to close our explanatory gap between cellular-level hypotheses and complex

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.

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

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.

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.

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.

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.

Neurosecurity: security and privacy for neural devices.

Science.gov (United States)

Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

2009-07-01

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

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.

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

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

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.

Mesocortical dopaminergic function and human cognition

International Nuclear Information System (INIS)

Weinberger, D.R.; Berman, K.F.; Chase, T.N.

1988-01-01

In summary, we have reviewed rCBF data in humans that suggest that mesoprefrontal dopaminergic activity is involved in human cognition. In patients with Parkinson's disease and possibly in patients with schizophrenia, prefrontal physiological activation during a cognitive task that appears to depend on prefrontal neural systems correlates positively with cognitive performance on the task and with clinical signs of dopaminergic function. It may be possible in the future to examine prefrontal dopamine metabolism directly during prefrontal cognition using positron emission tomography and tracers such as F-18 DOPA. 21 references

Functional and Anatomic Correlates of Neural Aging in Birds.

Science.gov (United States)

Ottinger, Mary Ann

2018-01-01

Avian species show variation in longevity, habitat, physiologic characteristics, and lifetime endocrine patterns. Lifetime reproductive and metabolic function vary. Much is known about the neurobiology of the song system in many altricial birds. Little is known about aging in neural systems in birds. Captive birds often survive beyond the age they would in the wild, providing an opportunity to gain an understanding of the physiologic and neural changes. This paper reviews the available information with the goal of capturing areas of potential investigation into gaps in our understanding of neural aging as reflected in physiologic, endocrine, and cognitive aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural Alterations in Acquired Age-Related Hearing Loss

Directory of Open Access Journals (Sweden)

Raksha Anand Mudar

2016-06-01

Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.

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

Introduction to neural networks

International Nuclear Information System (INIS)

Pavlopoulos, P.

1996-01-01

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

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.

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.

Neural fields theory and applications

CERN Document Server

Graben, Peter; Potthast, Roland; Wright, James

2014-01-01

With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

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.

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

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.

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

Architecture of cognitive flexibility revealed by lesion mapping

Science.gov (United States)

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

2013-01-01

Neuroscience has made remarkable progress in understanding the architecture of human intelligence, identifying a distributed network of brain structures that support goal-directed, intelligent behavior. However, the neural foundations of cognitive flexibility and adaptive aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 149) that investigates the neural bases of key competencies of cognitive flexibility (i.e., mental flexibility and the fluent generation of new ideas) and systematically examine their contributions to a broad spectrum of cognitive and social processes, including psychometric intelligence (Wechsler Adult Intelligence Scale), emotional intelligence (Mayer, Salovey, Caruso Emotional Intelligence Test), and personality (Neuroticism–Extraversion–Openness Personality Inventory). Latent variable modeling was applied to obtain error-free indices of each factor, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. Regression analyses revealed that latent scores for psychometric intelligence reliably predict latent scores for cognitive flexibility (adjusted R2 = 0.94). Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal, and parietal regions, including white matter association tracts, which bind these areas into an integrated system. A targeted analysis of the unique variance explained by cognitive flexibility further revealed selective damage within the right superior temporal gyrus, a region known to support insight and the recognition of novel semantic relations. The observed findings motivate an integrative framework for understanding the neural foundations of adaptive behavior, suggesting that core elements of cognitive flexibility emerge from a distributed network of brain regions that support specific competencies for human intelligence. PMID:23721727

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

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...

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.

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.

Playing Music for a Smarter Ear: Cognitive, Perceptual and Neurobiological Evidence

Science.gov (United States)

Strait, Dana; Kraus, Nina

2012-01-01

Human hearing depends on a combination of cognitive and sensory processes that function by means of an interactive circuitry of bottom-up and top-down neural pathways, extending from the cochlea to the cortex and back again. Given that similar neural pathways are recruited to process sounds related to both music and language, it is not surprising that the auditory expertise gained over years of consistent music practice fine-tunes the human auditory system in a comprehensive fashion, strengthening neurobiological and cognitive underpinnings of both music and speech processing. In this review we argue not only that common neural mechanisms for speech and music exist, but that experience in music leads to enhancements in sensory and cognitive contributors to speech processing. Of specific interest is the potential for music training to bolster neural mechanisms that undergird language-related skills, such as reading and hearing speech in background noise, which are critical to academic progress, emotional health, and vocational success. PMID:22993456

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

Learning and adaptation: neural and behavioural mechanisms behind behaviour change

Science.gov (United States)

Lowe, Robert; Sandamirskaya, Yulia

2018-01-01

This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

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.

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

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.

Uncovering the Neural Bases of Cognitive and Affective Empathy Deficits in Alzheimer's Disease and the Behavioral-Variant of Frontotemporal Dementia.

Science.gov (United States)

Dermody, Nadene; Wong, Stephanie; Ahmed, Rebekah; Piguet, Olivier; Hodges, John R; Irish, Muireann

2016-05-30

Loss of empathy is a core presenting feature of the behavioral-variant of frontotemporal dementia (bvFTD), resulting in socioemotional difficulties and behavioral transgressions. In contrast, interpersonal functioning remains relatively intact in Alzheimer's disease (AD), despite marked cognitive decline. The neural substrates mediating these patterns of loss and sparing in social functioning remain unclear, yet are relevant for our understanding of the social brain. We investigated cognitive versus affective aspects of empathy using the Interpersonal Reactivity Index (IRI) in 25 AD and 24 bvFTD patients and contrasted their performance with 22 age- and education-matched controls. Cognitive empathy was comparably compromised in AD and bvFTD, whereas affective empathy was impaired exclusively in bvFTD. While controlling for overall cognitive dysfunction ameliorated perspective-taking deficits in AD, empathy loss persisted across cognitive and affective domains in bvFTD. Voxel-based morphometry analyses revealed divergent neural substrates of empathy loss in each patient group. Perspective-taking deficits correlated with predominantly left-sided temporoparietal atrophy in AD, whereas widespread bilateral frontoinsular, temporal, parietal, and occipital atrophy was implicated in bvFTD. Reduced empathic concern in bvFTD was associated with atrophy in the left orbitofrontal, inferior frontal, and insular cortices, and the bilateral mid-cingulate gyrus. Our findings suggest that social cognitive deficits in AD arise largely as a consequence of global cognitive dysfunction, rather than a loss of empathy per se. In contrast, loss of empathy in bvFTD reflects the deterioration of a distributed network of frontoinsular and temporal structures that appear crucial for monitoring and processing social information.

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.

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...

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

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.

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.

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...

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.

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

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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.

Bio-inspired spiking neural network for nonlinear systems control.

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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.

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

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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.

Application of neural networks in CRM systems

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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.

Overlapping neural systems represent cognitive effort and reward anticipation.

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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.

Developmental insights into mature cognition.

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Keil, Frank C

2015-02-01

Three cases are described that illustrate new ways in which developmental research is informing the study of cognition in adults: statistical learning, neural substrates of cognition, and extended concepts. Developmental research has made clear the ubiquity of statistical learning while also revealing is limitations as a stand-alone way to acquire knowledge. With respect to neural substrates, development has uncovered links between executive processing and fronto-striatal circuits while also pointing to many aspects of high-level cognition that may not be neatly reducible to coherent neural descriptions. For extended concepts, children have made especially clear the weaknesses of intuitive theories in both children and adults while also illustrating other cognitive capacities that are used at all ages to navigate the socially distributed aspects of knowledge. Copyright © 2014 Elsevier B.V. All rights reserved.

Mind the fish: zebrafish as a model in cognitive social neuroscience

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Rui F Oliveira

2013-08-01

Full Text Available Understanding how the brain implements social behavior on one hand, and how social processes feedback on the brain to promote fine-tuning of behavioural output according to changes in the social environment is a major challenge in contemporary neuroscience. A critical step to take this challenge successfully is finding the appropriate level of analysis when relating social to biological phenomena. Given the enormous complexity of both the neural networks of the brain and social systems, the use of a cognitive level of analysis (in an information processing perspective is proposed here as an explanatory interface between brain and behavior. A conceptual framework for a cognitive approach to comparative social neuroscience is proposed, consisting of the following steps to be taken across different species with varying social systems: (1 identification of the functional building blocks of social skills; (2 identification of the cognitive mechanisms underlying the previously identified social skills; and (3 mapping these information processing mechanisms onto the brain. Teleost fish are presented here as a group of choice to develop this approach, given the diversity of social systems present in closely related species that allows for planned phylogenetic comparisons, and the availability of neurogenetic tools that allows the visualization and manipulation of selected neural circuits in model species such as the zebrafish. Finally, the state-of-the art of zebrafish social cognition and of the tools available to map social cognitive abilities to neural circuits in zebrafish are reviewed.

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....

Computerised working memory based cognitive remediation therapy does not affect Reading the Mind in the Eyes test performance or neural activity during a Facial Emotion Recognition test in psychosis.

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Mothersill, David; Dillon, Rachael; Hargreaves, April; Castorina, Marco; Furey, Emilia; Fagan, Andrew J; Meaney, James F; Fitzmaurice, Brian; Hallahan, Brian; McDonald, Colm; Wykes, Til; Corvin, Aiden; Robertson, Ian H; Donohoe, Gary

2018-05-27

Working memory based cognitive remediation therapy (CT) for psychosis has recently been associated with broad improvements in performance on untrained tasks measuring working memory, episodic memory and IQ, and changes in associated brain regions. However, it is unclear if these improvements transfer to the domain of social cognition and neural activity related to performance on social cognitive tasks. We examined performance on the Reading the Mind in the Eyes test (Eyes test) in a large sample of participants with psychosis who underwent working memory based CT (N = 43) compared to a Control Group of participants with psychosis (N = 35). In a subset of this sample, we used functional magnetic resonance imaging (fMRI) to examine changes in neural activity during a facial emotion recognition task in participants who underwent CT (N = 15) compared to a Control Group (N = 15). No significant effects of CT were observed on Eyes test performance or on neural activity during facial emotion recognition, either at pworking memory based CT does not significantly impact an aspect of social cognition which was measured behaviourally and neurally. It provides further evidence that deficits in the ability to decode mental state from facial expressions are dissociable from working memory deficits, and suggests that future CT programs should target social cognition in addition to working memory for the purposes of further enhancing social function. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Short-term synaptic plasticity and heterogeneity in neural systems

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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.

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

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.

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability.

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Armbruster-Genç, Diana J N; Ueltzhöffer, Kai; Fiebach, Christian J

2016-04-06

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories. Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential

Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler

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Griffiths, John D.

2015-12-01

The modern understanding of the brain as a large, complex network of interacting elements is a natural consequence of the Neuron Doctrine [1,2] that has been bolstered in recent years by the tools and concepts of connectomics. In this abstracted, network-centric view, the essence of neural and cognitive function derives from the flows between network elements of activity and information - or, more generally, causal influence. The appropriate characterization of causality in neural systems, therefore, is a question at the very heart of systems neuroscience.

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

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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

Spiking neural P systems with multiple channels.

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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.

COGNITIVE SYSTEMS. REDEFINING THE COOPERATION BETWEEN MAN AND SYSTEM

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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.

Brain insulin signaling: a key component of cognitive processes and a potential basis for cognitive impairment in type 2 diabetes

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McNay, Ewan C.; Recknagel, Andrew K.

2011-01-01

Understanding of the role of insulin in the brain has gradually expanded, from initial conceptions of the brain as insulin-insensitive through identification of a role in regulation of feeding to recent demonstration of insulin as a key component of hippocampal memory processes. Conversely, systemic insulin resistance such as that seen in type 2 diabetes is associated with a range of cogntive and neural deficits. Here we review the evidence for insulin as a cognitive and neural modulator, including potential effector mechanisms, and examine the impact that type 2 diabetes has on these mechanisms in order to identify likely bases for the cognitive impairments seen in type 2 diabetic patients. PMID:21907815

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

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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.

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.

Decoding the cognitive map: ensemble hippocampal sequences and decision making.

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Wikenheiser, Andrew M; Redish, A David

2015-06-01

Tolman proposed that complex animal behavior is mediated by the cognitive map, an integrative learning system that allows animals to reconfigure previous experience in order to compute predictions about the future. The discovery of place cells in the rodent hippocampus immediately suggested a plausible neural mechanism to fulfill the 'map' component of Tolman's theory. Recent work examining hippocampal representations occurring at fast time scales suggests that these sequences might be important for supporting the inferential mental operations associated with the cognitive map function. New findings that hippocampal sequences play an important causal role in mediating adaptive behavior on a moment-by-moment basis suggest specific neural processes that may underlie Tolman's cognitive map framework. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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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.

Integrating Functional Brain Neuroimaging and Developmental Cognitive Neuroscience in Child Psychiatry Research

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Pavuluri, Mani N.; Sweeney, John A.

2008-01-01

The use of cognitive neuroscience and functional brain neuroimaging to understand brain dysfunction in pediatric psychiatric disorders is discussed. Results show that bipolar youths demonstrate impairment in affective and cognitive neural systems and in these two circuits' interface. Implications for the diagnosis and treatment of psychiatric…

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

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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.

Autoimmunity as a Driving Force of Cognitive Evolution

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Serge Nataf

2017-10-01

Full Text Available In the last decades, increasingly robust experimental approaches have formally demonstrated that autoimmunity is a physiological process involved in a large range of functions including cognition. On this basis, the recently enunciated “brain superautoantigens” theory proposes that autoimmunity has been a driving force of cognitive evolution. It is notably suggested that the immune and nervous systems have somehow co-evolved and exerted a mutual selection pressure benefiting to both systems. In this two-way process, the evolutionary-determined emergence of neurons expressing specific immunogenic antigens (brain superautoantigens has exerted a selection pressure on immune genes shaping the T-cell repertoire. Such a selection pressure on immune genes has translated into the emergence of a finely tuned autoimmune T-cell repertoire that promotes cognition. In another hand, the evolutionary-determined emergence of brain-autoreactive T-cells has exerted a selection pressure on neural genes coding for brain superautoantigens. Such a selection pressure has translated into the emergence of a neural repertoire (defined here as the whole of neurons, synapses and non-neuronal cells involved in cognitive functions expressing brain superautoantigens. Overall, the brain superautoantigens theory suggests that cognitive evolution might have been primarily driven by internal cues rather than external environmental conditions. Importantly, while providing a unique molecular connection between neural and T-cell repertoires under physiological conditions, brain superautoantigens may also constitute an Achilles heel responsible for the particular susceptibility of Homo sapiens to “neuroimmune co-pathologies” i.e., disorders affecting both neural and T-cell repertoires. These may notably include paraneoplastic syndromes, multiple sclerosis as well as autism, schizophrenia and neurodegenerative diseases. In the context of this theoretical frame, a specific

Neural constructivism or self-organization?

NARCIS (Netherlands)

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

2000-01-01

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

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

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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.

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.

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

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.

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.

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

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.

Developmental Social Cognitive Neuroscience: Insights from Deafness

Science.gov (United States)

Corina, David; Singleton, Jenny

2009-01-01

The condition of deafness presents a developmental context that provides insight into the biological, cultural, and linguistic factors underlying the development of neural systems that impact social cognition. Studies of visual attention, behavioral regulation, language development, and face and human action perception are discussed. Visually…

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

Science.gov (United States)

Gallese, Vittorio

2008-01-01

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

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...

Literature Review of Cognitive Neuroscience and Anorexia Nervosa.

Science.gov (United States)

Reville, Marie-Claire; O'Connor, Lorna; Frampton, Ian

2016-02-01

Studies published between the beginning of 2013 and May 2015 on the neuropsychological functioning of patients with anorexia nervosa compared with healthy participants framed in the context of the Research Domain Criteria matrix identifies evidence for functional differences in three domains: Negative Valance Systems-negative attentional biases and lack of neural responsivity to hunger; Cognitive Systems-limited congruence between clinical and cognitive performance, poorer non-verbal than verbal performance, altered attentional styles to disorder related stimuli, perceptual processing impairment in discriminating body images, weaknesses in central coherence, set shifting weaknesses at low weight status, decision-making weaknesses, and greater neural resources required for working memory; Systems for Social Processes-patients appear to have a different attentional response to faces, and perception and understanding of self and others. Hence, there is evidence to suggest that patients with anorexia nervosa have a specific neuropsychological performance style across tasks in three domains of functioning. Some current controversies and areas for future development are identified.

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.

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

Cognon Neural Model Software Verification and Hardware Implementation Design

Science.gov (United States)

Haro Negre, Pau

Little is known yet about how the brain can recognize arbitrary sensory patterns within milliseconds using neural spikes to communicate information between neurons. In a typical brain there are several layers of neurons, with each neuron axon connecting to ˜104 synapses of neurons in an adjacent layer. The information necessary for cognition is contained in theses synapses, which strengthen during the learning phase in response to newly presented spike patterns. Continuing on the model proposed in "Models for Neural Spike Computation and Cognition" by David H. Staelin and Carl H. Staelin, this study seeks to understand cognition from an information theoretic perspective and develop potential models for artificial implementation of cognition based on neuronal models. To do so we focus on the mathematical properties and limitations of spike-based cognition consistent with existing neurological observations. We validate the cognon model through software simulation and develop concepts for an optical hardware implementation of a network of artificial neural cognons.

The impact of cognitive load on reward evaluation.

Science.gov (United States)

Krigolson, Olave E; Hassall, Cameron D; Satel, Jason; Klein, Raymond M

2015-11-19

The neural systems that afford our ability to evaluate rewards and punishments are impacted by a variety of external factors. Here, we demonstrate that increased cognitive load reduces the functional efficacy of a reward processing system within the human medial-frontal cortex. In our paradigm, two groups of participants used performance feedback to estimate the exact duration of one second while electroencephalographic (EEG) data was recorded. Prior to performing the time estimation task, both groups were instructed to keep their eyes still and avoid blinking in line with well established EEG protocol. However, during performance of the time-estimation task, one of the two groups was provided with trial-to-trial-feedback about their performance on the time-estimation task and their eye movements to induce a higher level of cognitive load relative to participants in the other group who were solely provided with feedback about the accuracy of their temporal estimates. In line with previous work, we found that the higher level of cognitive load reduced the amplitude of the feedback-related negativity, a component of the human event-related brain potential associated with reward evaluation within the medial-frontal cortex. Importantly, our results provide further support that increased cognitive load reduces the functional efficacy of a neural system associated with reward processing. Copyright © 2015 Elsevier B.V. All rights reserved.

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...

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.

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.

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.

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....

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.

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

Brain Plasticity and Motor Practice in Cognitive Aging

Directory of Open Access Journals (Sweden)

Liuyang eCai

2014-03-01

Full Text Available For more than two decades, there have been extensive studies of experience-based neural plasticity exploring effective applications of brain plasticity for cognitive and motor development. Research suggests that human brains continuously undergo structural reorganization and functional changes in response to stimulations or training. From a developmental point of view, the assumption of lifespan brain plasticity has been extended to older adults in terms of the benefits of cognitive training and physical therapy. To summarize recent developments, first, we introduce the concept of neural plasticity from a developmental perspective. Secondly, we note that motor learning often refers to deliberate practice and the resulting performance enhancement and adaptability. We discuss the close interplay between neural plasticity, motor learning and cognitive aging. Thirdly, we review research on motor skill acquisition in older adults with, and without, impairments relative to aging-related cognitive decline. Finally, to enhance future research and application, we highlight the implications of neural plasticity in skills learning and cognitive rehabilitation for the aging population.

Neural Substrates for Processing Task-Irrelevant Sad Images in Adolescents

Science.gov (United States)

Wang, Lihong; Huettel, Scott; De Bellis, Michael D.

2008-01-01

Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…

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.

Neurobiology of cognitive remediation therapy for schizophrenia: a systematic review.

Science.gov (United States)

Thorsen, Anders Lillevik; Johansson, Kyrre; Løberg, Else-Marie

2014-01-01

Cognitive impairment is an important aspect of schizophrenia, where cognitive remediation therapy (CRT) is a promising treatment for improving cognitive functioning. While neurobiological dysfunction in schizophrenia has been the target of much research, the neural substrate of cognitive remediation and recovery has not been thoroughly examined. The aim of the present article is to systematically review the evidence for neural changes after CRT for schizophrenia. The reviewed studies indicate that CRT affects several brain regions and circuits, including prefrontal, parietal, and limbic areas, both in terms of activity and structure. Changes in prefrontal areas are the most reported finding, fitting to previous evidence of dysfunction in this region. Two limitations of the current research are the few studies and the lack of knowledge on the mechanisms underlying neural and cognitive changes after treatment. Despite these limitations, the current evidence suggests that CRT is associated with both neurobiological and cognitive improvement. The evidence from these findings may shed light on both the neural substrate of cognitive impairment in schizophrenia, and how better treatment can be developed and applied.

Embodied artificial agents for understanding human social cognition.

Science.gov (United States)

Wykowska, Agnieszka; Chaminade, Thierry; Cheng, Gordon

2016-05-05

In this paper, we propose that experimental protocols involving artificial agents, in particular the embodied humanoid robots, provide insightful information regarding social cognitive mechanisms in the human brain. Using artificial agents allows for manipulation and control of various parameters of behaviour, appearance and expressiveness in one of the interaction partners (the artificial agent), and for examining effect of these parameters on the other interaction partner (the human). At the same time, using artificial agents means introducing the presence of artificial, yet human-like, systems into the human social sphere. This allows for testing in a controlled, but ecologically valid, manner human fundamental mechanisms of social cognition both at the behavioural and at the neural level. This paper will review existing literature that reports studies in which artificial embodied agents have been used to study social cognition and will address the question of whether various mechanisms of social cognition (ranging from lower- to higher-order cognitive processes) are evoked by artificial agents to the same extent as by natural agents, humans in particular. Increasing the understanding of how behavioural and neural mechanisms of social cognition respond to artificial anthropomorphic agents provides empirical answers to the conundrum 'What is a social agent?' © 2016 The Authors.

Neural Mechanisms of Selective Visual Attention.

Science.gov (United States)

Moore, Tirin; Zirnsak, Marc

2017-01-03

Selective visual attention describes the tendency of visual processing to be confined largely to stimuli that are relevant to behavior. It is among the most fundamental of cognitive functions, particularly in humans and other primates for whom vision is the dominant sense. We review recent progress in identifying the neural mechanisms of selective visual attention. We discuss evidence from studies of different varieties of selective attention and examine how these varieties alter the processing of stimuli by neurons within the visual system, current knowledge of their causal basis, and methods for assessing attentional dysfunctions. In addition, we identify some key questions that remain in identifying the neural mechanisms that give rise to the selective processing of visual information.

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.

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

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.

Electrophysiological Repetition Effects in Persons with Mild Cognitive Impairment depend upon Working Memory Demand.

Science.gov (United States)

Broster, Lucas S; Jenkins, Shonna L; Holmes, Sarah D; Edwards, Matthew G; Jicha, Gregory A; Jiang, Yang

2018-05-07

Forms of implicit memory, including repetition effects, are preserved relative to explicit memory in clinical Alzheimer's disease. Consequently, cognitive interventions for persons with Alzheimer's disease have been developed that leverage this fact. However, despite the clinical robustness of behavioral repetition effects, altered neural mechanisms of repetition effects are studied as biomarkers of both clinical Alzheimer's disease and pre-morbid Alzheimer's changes in the brain. We hypothesized that the clinical preservation of behavioral repetition effects results in part from concurrent operation of discrete memory systems. We developed two experiments that included probes of emotional repetition effects differing in that one included an embedded working memory task. We found that neural repetition effects manifested in patients with amnestic mild cognitive impairment, the earliest form of clinical Alzheimer's disease, during emotional working memory tasks, but they did not manifest during the task that lacked the embedded working memory manipulation. Specifically, the working memory task evoked neural repetition effects in the P600 time-window, but the same neural mechanism was only minimally implicated in the task without a working memory component. We also found that group differences in behavioral repetition effects were smaller in the experiment with a working memory task. We suggest that cross-domain cognitive challenge can expose "defunct" neural capabilities of individuals with amnestic mild cognitive impairment. Copyright © 2018. Published by Elsevier Ltd.

Implementation of a Space Communications Cognitive Engine

Science.gov (United States)

Hackett, Timothy M.; Bilen, Sven G.; Ferreira, Paulo Victor R.; Wyglinski, Alexander M.; Reinhart, Richard C.

2017-01-01

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

Nonlinear dynamics of the brain: emotion and cognition

Energy Technology Data Exchange (ETDEWEB)

Rabinovich, Mikhail I; Muezzinoglu, M K [Institute for Nonlinear Science, University of California, San Diego, CA (United States)

2010-07-08

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated. (reviews of topical problems)

Nonlinear dynamics of the brain: emotion and cognition

International Nuclear Information System (INIS)

Rabinovich, Mikhail I; Muezzinoglu, M K

2010-01-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated. (reviews of topical problems)

The neural signature of emotional memories in serial crimes.

Science.gov (United States)

Chassy, Philippe

2017-10-01

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

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.

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.

Integrating automatic and controlled processes into neurocognitive models of social cognition.

Science.gov (United States)

Satpute, Ajay B; Lieberman, Matthew D

2006-03-24

Interest in the neural systems underlying social perception has expanded tremendously over the past few decades. However, gaps between behavioral literatures in social perception and neuroscience are still abundant. In this article, we apply the concept of dual-process models to neural systems in an effort to bridge the gap between many of these behavioral studies and neural systems underlying social perception. We describe and provide support for a neural division between reflexive and reflective systems. Reflexive systems correspond to automatic processes and include the amygdala, basal ganglia, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, and lateral temporal cortex. Reflective systems correspond to controlled processes and include lateral prefrontal cortex, posterior parietal cortex, medial prefrontal cortex, rostral anterior cingulate cortex, and the hippocampus and surrounding medial temporal lobe region. This framework is considered to be a working model rather than a finished product. Finally, the utility of this model and its application to other social cognitive domains such as Theory of Mind are discussed.

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

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...

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.

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.

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.

Neural mechanisms tracking popularity in real-world social networks.

Science.gov (United States)

Zerubavel, Noam; Bearman, Peter S; Weber, Jochen; Ochsner, Kevin N

2015-12-08

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.

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

CERN Multimedia

CERN. Geneva

2016-01-01

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

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

CERN Multimedia

CERN. Geneva

2016-01-01

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

Two social brains: neural mechanisms of intersubjectivity.

Science.gov (United States)

Vogeley, Kai

2017-08-19

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

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

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

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.

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...

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...

The Impact of Obesity and Exercise on Cognitive Aging

Directory of Open Access Journals (Sweden)

John S.Y. eChan

2013-12-01

Full Text Available Obesity is a major concern in the aging population and degrades health, motor functions and cognition in older adults. The effects of obesity are pervasive and challenging to health-care systems, making this a widespread and critically important public health dilemma. In this review, we examine the relationship between obesity, cognitive aging, and related dysfunctions. Potential neural mechanisms underlying such relationship are described. We propose that cost-effective exercises can be employed to cope with obesity and cognitive declines in older adults. Finally, we discuss implications and future research directions.

A New Controller to Enhance PV System Performance Based on Neural Network

Directory of Open Access Journals (Sweden)

Roshdy A AbdelRassoul

2017-06-01

Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

Adaptive neural network/expert system that learns fault diagnosis for different structures

Science.gov (United States)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

Directory of Open Access Journals (Sweden)

Seth A. Herd

2013-01-01

Full Text Available We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC and basal ganglia (BG cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”. The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

The Impact of Obesity and Exercise on Cognitive Aging

OpenAIRE

Chan, John S. Y.; Yan, Jin H.; Payne, V. Gregory

2013-01-01

Obesity is a major concern in the aging population and degrades health, motor functions and cognition in older adults. The effects of obesity are pervasive and challenging to health-care systems, making this a widespread and critically important public health dilemma. In this review, we examine the relationship between obesity, cognitive aging, and related dysfunctions. Potential neural mechanisms underlying such relationship are described. We propose that cost-effective exercises can be empl...

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

Science.gov (United States)

Gallese, Vittorio; Cuccio, Valentina

2018-03-01

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

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.

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...

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

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

Towards a cognitive robotics methodology for reward-based decision-making: dynamical systems modelling of the Iowa Gambling Task

Science.gov (United States)

Lowe, Robert; Ziemke, Tom

2010-09-01

The somatic marker hypothesis (SMH) posits that the role of emotions and mental states in decision-making manifests through bodily responses to stimuli of import to the organism's welfare. The Iowa Gambling Task (IGT), proposed by Bechara and Damasio in the mid-1990s, has provided the major source of empirical validation to the role of somatic markers in the service of flexible and cost-effective decision-making in humans. In recent years the IGT has been the subject of much criticism concerning: (1) whether measures of somatic markers reveal that they are important for decision-making as opposed to behaviour preparation; (2) the underlying neural substrate posited as critical to decision-making of the type relevant to the task; and (3) aspects of the methodological approach used, particularly on the canonical version of the task. In this paper, a cognitive robotics methodology is proposed to explore a dynamical systems approach as it applies to the neural computation of reward-based learning and issues concerning embodiment. This approach is particularly relevant in light of a strongly emerging alternative hypothesis to the SMH, the reversal learning hypothesis, which links, behaviourally and neurocomputationally, a number of more or less complex reward-based decision-making tasks, including the 'A-not-B' task - already subject to dynamical systems investigations with a focus on neural activation dynamics. It is also suggested that the cognitive robotics methodology may be used to extend systematically the IGT benchmark to more naturalised, but nevertheless controlled, settings that might better explore the extent to which the SMH, and somatic states per se, impact on complex decision-making.

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

Science.gov (United States)

Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

2014-01-01

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

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.

Adaptive Synchronization of Memristor-based Chaotic Neural Systems

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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.

The neural correlates of visuo-spatial working memory in children with autism spectrum disorder: effects of cognitive load.

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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

Imaging Posture Veils Neural Signals

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Robert T Thibault

2016-10-01

Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

Video game training enhances cognitive control in older adults.

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Anguera, J A; Boccanfuso, J; Rintoul, J L; Al-Hashimi, O; Faraji, F; Janowich, J; Kong, E; Larraburo, Y; Rolle, C; Johnston, E; Gazzaley, A

2013-09-05

Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generating interference as the result of fundamental information processing limitations. It is clear that multitasking behaviour has become ubiquitous in today's technologically dense world, and substantial evidence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population. Here we show that multitasking performance, as assessed with a custom-designed three-dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalography, were remediated by multitasking training (enhanced midline frontal theta power and frontal-posterior theta coherence). Critically, this training resulted in performance benefits that extended to untrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings highlight the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, of how a custom-designed video game can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool

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)

Imbalanced functional link between reward circuits and the cognitive control system in patients with obsessive-compulsive disorder.

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Xie, Chunming; Ma, Lisha; Jiang, Nan; Huang, Ruyan; Li, Li; Gong, Liang; He, Cancan; Xiao, Chaoyong; Liu, Wen; Xu, Shu; Zhang, Zhijun

2017-08-01

Altered reward processing and cognitive deficits are often observed in patients with obsessive-compulsive disorder (OCD); however, whether the imbalance in activity between reward circuits and the cognitive control (CC) system is associated with compulsive behavior remains unknown. Sixty-eight OCD patients and 33 cognitively normal (CN) healthy subjects participated in this resting-state functional magnetic resonance imaging study. Alterations in the functional connectivity between reward circuits and the CC system were quantitatively assessed and compared between the groups. A Granger causality analysis was used to determine the causal informational influence between and within reward circuits and the CC system across all subjects. OCD patients showed a dichotomous pattern of enhanced functional coupling in their reward circuits and a weakened functional coupling in their CC system when compared to CN subjects. Neural correlates of compulsive behavior were primarily located in the reward circuits and CC system in OCD patients. Importantly, the CC system exerted a reduced interregional causal influence over the reward system in OCD patients relative to its effect in CN subjects. The limitations of this study are that it was a cross-sectional study and the potential effects of environmental and genetic factors were not explored. OCD patients showed an imbalance in the functional link between reward circuits and the CC system at rest. This bias toward a loss of control may define a pathological state in which subjects are more vulnerable to engaging in compulsive behaviors.

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

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...

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.

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.

Neural correlates of eating disorders: translational potential

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McAdams CJ

2015-09-01

Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

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

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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.

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.

Extended Cognitive System and Epistemic Subject

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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”.

Frequency-dependent oscillatory neural profiles during imitation.

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Sugata, Hisato; Hirata, Masayuki; Tamura, Yuichi; Onishi, Hisao; Goto, Tetsu; Araki, Toshihiko; Yorifuji, Shiro

2017-04-10

Imitation is a complex process that includes higher-order cognitive and motor function. This process requires an observation-execution matching system that transforms an observed action into an identical movement. Although the low-gamma band is thought to reflect higher cognitive processes, no studies have focused on it. Here, we used magnetoencephalography (MEG) to examine the neural oscillatory changes including the low-gamma band during imitation. Twelve healthy, right-handed participants performed a finger task consisting of four conditions (imitation, execution, observation, and rest). During the imitation and execution conditions, significant event-related desynchronizations (ERDs) were observed at the left frontal, central, and parietal MEG sensors in the alpha, beta, and low-gamma bands. Functional connectivity analysis at the sensor level revealed an imitation-related connectivity between a group of frontal sensors and a group of parietal sensors in the low-gamma band. Furthermore, source reconstruction with synthetic aperture magnetometry showed significant ERDs in the low-gamma band in the left sensorimotor area and the middle frontal gyrus (MFG) during the imitation condition when compared with the other three conditions. Our results suggest that the oscillatory neural activities of the low-gamma band at the sensorimotor area and MFG play an important role in the observation-execution matching system related to imitation.

Frequency-dependent oscillatory neural profiles during imitation

Science.gov (United States)

Sugata, Hisato; Hirata, Masayuki; Tamura, Yuichi; Onishi, Hisao; Goto, Tetsu; Araki, Toshihiko; Yorifuji, Shiro

2017-01-01

Imitation is a complex process that includes higher-order cognitive and motor function. This process requires an observation-execution matching system that transforms an observed action into an identical movement. Although the low-gamma band is thought to reflect higher cognitive processes, no studies have focused on it. Here, we used magnetoencephalography (MEG) to examine the neural oscillatory changes including the low-gamma band during imitation. Twelve healthy, right-handed participants performed a finger task consisting of four conditions (imitation, execution, observation, and rest). During the imitation and execution conditions, significant event-related desynchronizations (ERDs) were observed at the left frontal, central, and parietal MEG sensors in the alpha, beta, and low-gamma bands. Functional connectivity analysis at the sensor level revealed an imitation-related connectivity between a group of frontal sensors and a group of parietal sensors in the low-gamma band. Furthermore, source reconstruction with synthetic aperture magnetometry showed significant ERDs in the low-gamma band in the left sensorimotor area and the middle frontal gyrus (MFG) during the imitation condition when compared with the other three conditions. Our results suggest that the oscillatory neural activities of the low-gamma band at the sensorimotor area and MFG play an important role in the observation-execution matching system related to imitation. PMID:28393878

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.

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.

Neural and behavioural changes in male periadolescent mice after prolonged nicotine-MDMA treatment.

Science.gov (United States)

Adeniyi, Philip A; Ishola, Azeez O; Laoye, Babafemi J; Olatunji, Babawale P; Bankole, Oluwamolakun O; Shallie, Philemon D; Ogundele, Olalekan M

2016-02-01

The interaction between MDMA and Nicotine affects multiple brain centres and neurotransmitter systems (serotonin, dopamine and glutamate) involved in motor coordination and cognition. In this study, we have elucidated the effect of prolonged (10 days) MDMA, Nicotine and a combined Nicotine-MDMA treatment on motor-cognitive neural functions. In addition, we have shown the correlation between the observed behavioural change and neural structural changes induced by these treatments in BALB/c mice. We observed that MDMA (2 mg/Kg body weight; subcutaneous) induced a decline in motor function, while Nicotine (2 mg/Kg body weight; subcutaneous) improved motor function in male periadolescent mice. In combined treatment, Nicotine reduced the motor function decline observed in MDMA treatment, thus no significant change in motor function for the combined treatment versus the control. Nicotine or MDMA treatment reduced memory function and altered hippocampal structure. Similarly, a combined Nicotine-MDMA treatment reduced memory function when compared with the control. Ultimately, the metabolic and structural changes in these neural systems were seen to vary for the various forms of treatment. It is noteworthy to mention that a combined treatment increased the rate of lipid peroxidation in brain tissue.

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.

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.

Cognitive neuroscience robotics B analytic approaches to human understanding

CERN Document Server

Ishiguro, Hiroshi; Asada, Minoru; Osaka, Mariko; Fujikado, Takashi

2016-01-01

Cognitive Neuroscience Robotics is the first introductory book on this new interdisciplinary area. This book consists of two volumes, the first of which, Synthetic Approaches to Human Understanding, advances human understanding from a robotics or engineering point of view. The second, Analytic Approaches to Human Understanding, addresses related subjects in cognitive science and neuroscience. These two volumes are intended to complement each other in order to more comprehensively investigate human cognitive functions, to develop human-friendly information and robot technology (IRT) systems, and to understand what kind of beings we humans are. Volume B describes to what extent cognitive science and neuroscience have revealed the underlying mechanism of human cognition, and investigates how development of neural engineering and advances in other disciplines could lead to deep understanding of human cognition.

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.

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

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

Science.gov (United States)

Jasinska, K K; Petitto, L A

2013-10-01

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

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.

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.

Development of cognitive and affective control networks and decision making.

Science.gov (United States)

Kar, Bhoomika R; Vijay, Nivita; Mishra, Shreyasi

2013-01-01

Cognitive control and decision making are two important research areas in the realm of higher-order cognition. Control processes such as interference control and monitoring in cognitive and affective contexts have been found to influence the process of decision making. Development of control processes follows a gradual growth pattern associated with the prolonged maturation of underlying neural circuits including the lateral prefrontal cortex, anterior cingulate, and the medial prefrontal cortex. These circuits are also involved in the control of processes that influences decision making, particularly with respect to choice behavior. Developmental studies on affective control have shown distinct patterns of brain activity with adolescents showing greater activation of amygdala whereas adults showing greater activity in ventral prefrontal cortex. Conflict detection, monitoring, and adaptation involve anticipation and subsequent performance adjustments which are also critical to complex decision making. We discuss the gradual developmental patterns observed in two of our studies on conflict monitoring and adaptation in affective and nonaffective contexts. Findings of these studies indicate the need to look at the differences in the effects of the development of cognitive and affective control on decision making in children and particularly adolescents. Neuroimaging studies have shown the involvement of separable neural networks for cognitive (medial prefrontal cortex and anterior cingulate) and affective control (amygdala, ventral medial prefrontal cortex) shows that one system can affect the other also at the neural level. Hence, an understanding of the interaction and balance between the cognitive and affective brain networks may be crucial for self-regulation and decision making during the developmental period, particularly late childhood and adolescence. The chapter highlights the need for empirical investigation on the interaction between the different aspects

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...

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)

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.

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.

Consciousness, cognition and brain networks: New perspectives.

Science.gov (United States)

Aldana, E M; Valverde, J L; Fábregas, N

2016-10-01

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

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

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

Implications of newborn amygdala connectivity for fear and cognitive development at 6-months-of-age

Science.gov (United States)

Graham, Alice M.; Buss, Claudia; Rasmussen, Jerod M.; Rudolph, Marc D.; Demeter, Damion V.; Gilmore, John H.; Styner, Martin; Entringer, Sonja; Wadhwa, Pathik D.; Fair, Damien A.

2015-01-01

The first year of life is an important period for emergence of fear in humans. While animal models have revealed developmental changes in amygdala circuitry accompanying emerging fear, human neural systems involved in early fear development remain poorly understood. To increase understanding of the neural foundations of human fear, it is important to consider parallel cognitive development, which may modulate associations between typical development of early fear and subsequent risk for fear-related psychopathology. We, therefore, examined amygdala functional connectivity with rs-fcMRI in 48 neonates (M=3.65 weeks, SD=1.72), and measured fear and cognitive development at 6-months-of-age. Stronger, positive neonatal amygdala connectivity to several regions, including bilateral anterior insula and ventral striatum, was prospectively associated with higher fear at 6-months. Stronger amygdala connectivity to ventral anterior cingulate/anterior medial prefrontal cortex predicted a specific phenotype of higher fear combined with more advanced cognitive development. Overall, findings demonstrate unique profiles of neonatal amygdala functional connectivity related to emerging fear and cognitive development, which may have implications for normative and pathological fear in later years. Consideration of infant fear in the context of cognitive development will likely contribute to a more nuanced understanding of fear, its neural bases, and its implications for future mental health. PMID:26499255

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

Keller, James M; Fogel, David B

2016-01-01

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

Precursors to language: Social cognition and pragmatic inference in primates.

Science.gov (United States)

Seyfarth, Robert M; Cheney, Dorothy L

2017-02-01

Despite their differences, human language and the vocal communication of nonhuman primates share many features. Both constitute forms of coordinated activity, rely on many shared neural mechanisms, and involve discrete, combinatorial cognition that includes rich pragmatic inference. These common features suggest that during evolution the ancestors of all modern primates faced similar social problems and responded with similar systems of communication and cognition. When language later evolved from this common foundation, many of its distinctive features were already present.

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...

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.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

Science.gov (United States)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

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.

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.

A New Measure for Neural Compensation Is Positively Correlated With Working Memory and Gait Speed.

Science.gov (United States)

Ji, Lanxin; Pearlson, Godfrey D; Hawkins, Keith A; Steffens, David C; Guo, Hua; Wang, Lihong

2018-01-01

Neuroimaging studies suggest that older adults may compensate for declines in brain function and cognition through reorganization of neural resources. A limitation of prior research is reliance on between-group comparisons of neural activation (e.g., younger vs. older), which cannot be used to assess compensatory ability quantitatively. It is also unclear about the relationship between compensatory ability with cognitive function or how other factors such as physical exercise modulates compensatory ability. Here, we proposed a data-driven method to semi-quantitatively measure neural compensation under a challenging cognitive task, and we then explored connections between neural compensation to cognitive engagement and cognitive reserve (CR). Functional and structural magnetic resonance imaging scans were acquired for 26 healthy older adults during a face-name memory task. Spatial independent component analysis (ICA) identified visual, attentional and left executive as core networks. Results show that the smaller the volumes of the gray matter (GM) structures within core networks, the more networks were needed to conduct the task ( r = -0.408, p = 0.035). Therefore, the number of task-activated networks controlling for the GM volume within core networks was defined as a measure of neural compensatory ability. We found that compensatory ability correlated with working memory performance ( r = 0.528, p = 0.035). Among subjects with good memory task performance, those with higher CR used fewer networks than subjects with lower CR. Among poor-performance subjects, those using more networks had higher CR. Our results indicated that using a high cognitive-demanding task to measure the number of activated neural networks could be a useful and sensitive measure of neural compensation in older adults.

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.

Automated implementation of rule-based expert systems with neural networks for time-critical applications

Science.gov (United States)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Neural systems analysis of decision making during goal-directed navigation.

Science.gov (United States)

Penner, Marsha R; Mizumori, Sheri J Y

2012-01-01

The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.

Intelligent neural network and fuzzy logic control of industrial and power systems

Science.gov (United States)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

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.

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.

Music enrichment programs improve the neural encoding of speech in at-risk children.

Science.gov (United States)

Kraus, Nina; Slater, Jessica; Thompson, Elaine C; Hornickel, Jane; Strait, Dana L; Nicol, Trent; White-Schwoch, Travis

2014-09-03

Musicians are often reported to have enhanced neurophysiological functions, especially in the auditory system. Musical training is thought to improve nervous system function by focusing attention on meaningful acoustic cues, and these improvements in auditory processing cascade to language and cognitive skills. Correlational studies have reported musician enhancements in a variety of populations across the life span. In light of these reports, educators are considering the potential for co-curricular music programs to provide auditory-cognitive enrichment to children during critical developmental years. To date, however, no studies have evaluated biological changes following participation in existing, successful music education programs. We used a randomized control design to investigate whether community music participation induces a tangible change in auditory processing. The community music training was a longstanding and successful program that provides free music instruction to children from underserved backgrounds who stand at high risk for learning and social problems. Children who completed 2 years of music training had a stronger neurophysiological distinction of stop consonants, a neural mechanism linked to reading and language skills. One year of training was insufficient to elicit changes in nervous system function; beyond 1 year, however, greater amounts of instrumental music training were associated with larger gains in neural processing. We therefore provide the first direct evidence that community music programs enhance the neural processing of speech in at-risk children, suggesting that active and repeated engagement with sound changes neural function. Copyright © 2014 the authors 0270-6474/14/3411913-06$15.00/0.

REVIEWS OF TOPICAL PROBLEMS: Nonlinear dynamics of the brain: emotion and cognition

Science.gov (United States)

Rabinovich, Mikhail I.; Muezzinoglu, M. K.

2010-07-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated.

Self-reported empathy and neural activity during action imitation and observation in schizophrenia.

Science.gov (United States)

Horan, William P; Iacoboni, Marco; Cross, Katy A; Korb, Alex; Lee, Junghee; Nori, Poorang; Quintana, Javier; Wynn, Jonathan K; Green, Michael F

2014-01-01

Although social cognitive impairments are key determinants of functional outcome in schizophrenia their neural bases are poorly understood. This study investigated neural activity during imitation and observation of finger movements and facial expressions in schizophrenia, and their correlates with self-reported empathy. 23 schizophrenia outpatients and 23 healthy controls were studied with functional magnetic resonance imaging (fMRI) while they imitated, executed, or simply observed finger movements and facial emotional expressions. Between-group activation differences, as well as relationships between activation and self-reported empathy, were evaluated. Both patients and controls similarly activated neural systems previously associated with these tasks. We found no significant between-group differences in task-related activations. There were, however, between-group differences in the correlation between self-reported empathy and right inferior frontal (pars opercularis) activity during observation of facial emotional expressions. As in previous studies, controls demonstrated a positive association between brain activity and empathy scores. In contrast, the pattern in the patient group reflected a negative association between brain activity and empathy. Although patients with schizophrenia demonstrated largely normal patterns of neural activation across the finger movement and facial expression tasks, they reported decreased self perceived empathy and failed to show the typical relationship between neural activity and self-reported empathy seen in controls. These findings suggest that patients show a disjunction between automatic neural responses to low level social cues and higher level, integrative social cognitive processes involved in self-perceived empathy.

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

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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.

Encoding and Decoding Models in Cognitive Electrophysiology

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Christopher R. Holdgraf

2017-09-01

Full Text Available Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

Science.gov (United States)

Walter, Florian; Röhrbein, Florian; Knoll, Alois

2015-12-01

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bilingualism provides a neural reserve for aging populations.

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Abutalebi, Jubin; Guidi, Lucia; Borsa, Virginia; Canini, Matteo; Della Rosa, Pasquale A; Parris, Ben A; Weekes, Brendan S

2015-03-01

It has been postulated that bilingualism may act as a cognitive reserve and recent behavioral evidence shows that bilinguals are diagnosed with dementia about 4-5 years later compared to monolinguals. In the present study, we investigated the neural basis of these putative protective effects in a group of aging bilinguals as compared to a matched monolingual control group. For this purpose, participants completed the Erikson Flanker task and their performance was correlated to gray matter (GM) volume in order to investigate if cognitive performance predicts GM volume specifically in areas affected by aging. We performed an ex-Gaussian analysis on the resulting RTs and report that aging bilinguals performed better than aging monolinguals on the Flanker task. Bilingualism was overall associated with increased GM in the ACC. Likewise, aging induced effects upon performance correlated only for monolinguals to decreased gray matter in the DLPFC. Taken together, these neural regions might underlie the benefits of bilingualism and act as a neural reserve that protects against the cognitive decline that occurs during aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent.

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Moioli, Renan C; Vargas, Patricia A; Husbands, Phil

2012-09-01

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

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

Directory of Open Access Journals (Sweden)

S. Lee Hong

2012-09-01

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

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.

Optimal path-finding through mental exploration based on neural energy field gradients.

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Wang, Yihong; Wang, Rubin; Zhu, Yating

2017-02-01

Rodent animal can accomplish self-locating and path-finding task by forming a cognitive map in the hippocampus representing the environment. In the classical model of the cognitive map, the system (artificial animal) needs large amounts of physical exploration to study spatial environment to solve path-finding problems, which costs too much time and energy. Although Hopfield's mental exploration model makes up for the deficiency mentioned above, the path is still not efficient enough. Moreover, his model mainly focused on the artificial neural network, and clear physiological meanings has not been addressed. In this work, based on the concept of mental exploration, neural energy coding theory has been applied to the novel calculation model to solve the path-finding problem. Energy field is constructed on the basis of the firing power of place cell clusters, and the energy field gradient can be used in mental exploration to solve path-finding problems. The study shows that the new mental exploration model can efficiently find the optimal path, and present the learning process with biophysical meaning as well. We also analyzed the parameters of the model which affect the path efficiency. This new idea verifies the importance of place cell and synapse in spatial memory and proves that energy coding is effective to study cognitive activities. This may provide the theoretical basis for the neural dynamics mechanism of spatial memory.

Neural networks for feedback feedforward nonlinear control systems.

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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.

An information theory account of cognitive control.

Science.gov (United States)

Fan, Jin

2014-01-01

Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.

An information theory account of cognitive control

Directory of Open Access Journals (Sweden)

Jin eFan

2014-09-01

Full Text Available Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

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Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance.

Science.gov (United States)

Kable, Joseph W; Caulfield, M Kathleen; Falcone, Mary; McConnell, Mairead; Bernardo, Leah; Parthasarathi, Trishala; Cooper, Nicole; Ashare, Rebecca; Audrain-McGovern, Janet; Hornik, Robert; Diefenbach, Paul; Lee, Frank J; Lerman, Caryn

2017-08-02

Increased preference for immediate over delayed rewards and for risky over certain rewards has been associated with unhealthy behavioral choices. Motivated by evidence that enhanced cognitive control can shift choice behavior away from immediate and risky rewards, we tested whether training executive cognitive function could influence choice behavior and brain responses. In this randomized controlled trial, 128 young adults (71 male, 57 female) participated in 10 weeks of training with either a commercial web-based cognitive training program or web-based video games that do not specifically target executive function or adapt the level of difficulty throughout training. Pretraining and post-training, participants completed cognitive assessments and functional magnetic resonance imaging during performance of the following validated decision-making tasks: delay discounting (choices between smaller rewards now vs larger rewards in the future) and risk sensitivity (choices between larger riskier rewards vs smaller certain rewards). Contrary to our hypothesis, we found no evidence that cognitive training influences neural activity during decision-making; nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity. Participants in the commercial training condition improved with practice on the specific tasks they performed during training, but participants in both conditions showed similar improvement on standardized cognitive measures over time. Moreover, the degree of improvement was comparable to that observed in individuals who were reassessed without any training whatsoever. Commercial adaptive cognitive training appears to have no benefits in healthy young adults above those of standard video games for measures of brain activity, choice behavior, or cognitive performance. SIGNIFICANCE STATEMENT Engagement of neural regions and circuits important in executive cognitive function can bias behavioral choices away from immediate

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.

Estrogens and cognition: Friends or foes?: An evaluation of the opposing effects of estrogens on learning and memory.

Science.gov (United States)

Korol, Donna L; Pisani, Samantha L

2015-08-01

This article is part of a Special Issue "Estradiol and cognition". Estrogens are becoming well known for their robust enhancement on cognition particularly for learning and memory that relies upon functioning of the hippocampus and related neural systems. What is also emerging is that estrogen modulation of cognition is not uniform, at times enhancing yet at other times impairing learning. This review explores the bidirectional effects of estrogens on learning from a multiple memory systems view, focusing on the hippocampus and striatum, whereby modulation by estrogens sorts according to task attributes and neural systems engaged during cognition. We highlight our findings showing that the ability to solve hippocampus-sensitive tasks typically improves under relatively high estrogen status while the ability to solve striatum-sensitive tasks degrades with estrogen exposures. Though constrained by dose and timing of exposure, these opposing enhancements and impairments of cognition can be observed following treatments with different estrogenic compounds including the hormone estradiol, the isoflavone genistein found in soybeans, and agonists that are selective for specific estrogen receptors, suggesting that activation of a single receptor type is sufficient to produce the observed shifts in learning strategies. Using this multi-dimensional framework will allow us to extend our thinking of the relationship between estrogens and cognition to other brain regions and cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.

Approaches to analysis in model-based cognitive neuroscience

NARCIS (Netherlands)

Turner, B.M.; Forstmann, B.U.; Love, B.C.; Palmeri, T.J.; Van Maanen, L.

Our understanding of cognition has been advanced by two traditionally non-overlapping and non-interacting groups. Mathematical psychologists rely on behavioral data to evaluate formal models of cognition, whereas cognitive neuroscientists rely on statistical models to understand patterns of neural

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

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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.

Cognitive Neuroscience Approaches to Understanding Behavior Change in Alcohol Use Disorder Treatments.

Science.gov (United States)

Naqvi, Nasir H; Morgenstern, Jon

2015-01-01

Researchers have begun to apply cognitive neuroscience concepts and methods to study behavior change mechanisms in alcohol use disorder (AUD) treatments. This review begins with an examination of the current state of treatment mechanisms research using clinical and social psychological approaches. It then summarizes what is currently understood about the pathophysiology of addiction from a cognitive neuroscience perspective. Finally, it reviews recent efforts to use cognitive neuroscience approaches to understand the neural mechanisms of behavior change in AUD, including studies that use neural functioning to predict relapse and abstinence; studies examining neural mechanisms that operate in current evidence-based behavioral interventions for AUD; as well as research on novel behavioral interventions that are being derived from our emerging understanding of the neural and cognitive mechanisms of behavior change in AUD. The article highlights how the regulation of subcortical regions involved in alcohol incentive motivation by prefrontal cortical regions involved in cognitive control may be a core mechanism that plays a role in these varied forms of behavior change in AUD. We also lay out a multilevel framework for integrating cognitive neuroscience approaches with more traditional methods for examining AUD treatment mechanisms.

Chronic stress and neural function: accounting for sex and age.

Science.gov (United States)

Luine, V N; Beck, K D; Bowman, R E; Frankfurt, M; Maclusky, N J

2007-10-01

Cognitive responses to stress follow the temporally dependent pattern originally established by Selye (1) wherein short-term stressors elicit adaptive responses whereas continued stress (chronic) results in maladaptive changes--deleterious effects on physiological systems and impaired cognition. However, this pattern for cognitive effects appears to apply to only half the population (males) and, more specifically, to young, adult males. Females show different cognitive responses to stress. In contrast to impaired cognition in males after chronic stress, female rodents show enhanced performance on the same memory tasks after the same stress. Not only cognition, but anxiety, shows sex-dependent changes following chronic stress--stress is anxiolytic in males and anxiogenic in females. Moreover, behavioral responses to chronic stress are different in developing as well as aging subjects (both sexes) as compared to adults. In aged rats, chronic stress enhances recognition memory in both sexes, does not alter spatial memory, and anxiety effects are opposite to young adults. When pregnant dams are exposed to chronic stress, at adulthood the offspring display yet different consequences of stress on anxiety and cognition, and, in contrast to adulthood when the behavioral effects of stress are reversible, prenatal stress effects appear enduring. Changing levels of estradiol in the sexes over the lifespan appear to contribute to the differences in response to stress. Thus, theories of stress dependent modulations in CNS function--developed solely in male models, focused on peripheral physiological processes and tested in adults--may require revision when applied to a more diverse population (age- and sex-wise) at least in relation to the neural functions of cognition and anxiety. Moreover, these results suggest that other stressors and neural functions should be investigated to determine whether age, sex and gonadal hormones also have an impact.

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.

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.

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...

Review On Applications Of Neural Network To Computer Vision

Science.gov (United States)

Li, Wei; Nasrabadi, Nasser M.

1989-03-01

Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

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...

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.

Neural basis of individualistic and collectivistic views of self.

Science.gov (United States)

Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

2009-09-01

Individualism and collectivism refer to cultural values that influence how people construe themselves and their relation to the world. Individualists perceive themselves as stable entities, autonomous from other people and their environment, while collectivists view themselves as dynamic entities, continually defined by their social context and relationships. Despite rich understanding of how individualism and collectivism influence social cognition at a behavioral level, little is known about how these cultural values modulate neural representations underlying social cognition. Using cross-cultural functional magnetic resonance imaging (fMRI), we examined whether the cultural values of individualism and collectivism modulate neural activity within medial prefrontal cortex (MPFC) during processing of general and contextual self judgments. Here, we show that neural activity within the anterior rostral portion of the MPFC during processing of general and contextual self judgments positively predicts how individualistic or collectivistic a person is across cultures. These results reveal two kinds of neural representations of self (eg, a general self and a contextual self) within MPFC and demonstrate how cultural values of individualism and collectivism shape these neural representations. 2008 Wiley-Liss, Inc.

Buffering social influence: neural correlates of response inhibition predict driving safety in the presence of a peer.

Science.gov (United States)

Cascio, Christopher N; Carp, Joshua; O'Donnell, Matthew Brook; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G; Falk, Emily B

2015-01-01

Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.

Container-code recognition system based on computer vision and deep neural networks

Science.gov (United States)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

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

Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

Science.gov (United States)

Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

2011-11-01

Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

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)

Stress, glucocorticoid hormones, and hippocampal neural progenitor cells: implications to mood disorders.