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.

Dissociable neural representations of grammatical gender in Broca's area investigated by the combination of satiation and TMS.

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Cattaneo, Zaira; Devlin, Joseph T; Vecchi, Tomaso; Silvanto, Juha

2009-08-15

Along with meaning and form, words can be described on the basis of their grammatical properties. Grammatical gender is often used to investigate the latter as it is a grammatical property that is independent of meaning. The left inferior frontal gyrus (IFG) has been implicated in the encoding of grammatical gender, but its causal role in this process in neurologically normal observers has not been demonstrated. Here we combined verbal satiation with transcranial magnetic stimulation (TMS) to demonstrate that subpopulations of neurons within Broca's area respond preferentially to different classes of grammatical gender. Subjects were asked to classify Italian nouns into living and nonliving categories; half of these words were of masculine and the other half of feminine grammatical gender. Prior to each test block, a satiation paradigm (a phenomenon in which verbal repetition of a category name leads to a reduced access to that category) was used to modulate the initial state of the representations of either masculine or feminine noun categories. In the No TMS condition, subjects were slower in responding to exemplars to the satiated category relative to exemplars of the nonsatiated category, implying that the neural representations for different classes of grammatical gender are partly dissociable. The application of TMS over Broca's area removed the behavioral impact of verbal (grammatical) satiation, demonstrating the causal role of this region in the encoding of grammatical gender. These results show that the neural representations for different cases of a grammatical property within Broca's area are dissociable.

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.

Recollection of episodic memory within the medial temporal lobe: behavioural dissociations from other types of memory.

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Easton, Alexander; Eacott, Madeline J

2010-12-31

In recent years there has been significant debate about whether there is a single medial temporal lobe memory system or dissociable systems for episodic and other types of declarative memory. In addition there has been a similar debate over the dissociability of recollection and familiarity based processes in recognition memory. Here we present evidence from recent work using episodic memory tasks in animals that allows us to explore these issues in more depth. We review studies that demonstrate triple dissociations within the medial temporal lobe, with only the hippocampal system being necessary for episodic memory. Similarly we review behavioural evidence for a dissociation in a task of episodic memory in rats where animals with lesions of the fornix are only impaired at recollection of the episodic memory, not recognition within the same trial. This work, then, supports recent models of dissociable neural systems within the medial temporal lobe but also raises questions for future investigation about the interactions of these medial temporal lobe memory systems with other structures. Copyright © 2009 Elsevier B.V. All rights reserved.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

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Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Dissociating sensory from decision processes in human perceptual decision making.

Science.gov (United States)

Mostert, Pim; Kok, Peter; de Lange, Floris P

2015-12-15

A key question within systems neuroscience is how the brain translates physical stimulation into a behavioral response: perceptual decision making. To answer this question, it is important to dissociate the neural activity underlying the encoding of sensory information from the activity underlying the subsequent temporal integration into a decision variable. Here, we adopted a decoding approach to empirically assess this dissociation in human magnetoencephalography recordings. We used a functional localizer to identify the neural signature that reflects sensory-specific processes, and subsequently traced this signature while subjects were engaged in a perceptual decision making task. Our results revealed a temporal dissociation in which sensory processing was limited to an early time window and consistent with occipital areas, whereas decision-related processing became increasingly pronounced over time, and involved parietal and frontal areas. We found that the sensory processing accurately reflected the physical stimulus, irrespective of the eventual decision. Moreover, the sensory representation was stable and maintained over time when it was required for a subsequent decision, but unstable and variable over time when it was task-irrelevant. In contrast, decision-related activity displayed long-lasting sustained components. Together, our approach dissects neuro-anatomically and functionally distinct contributions to perceptual decisions.

Dissociating sensory from decision processes in human perceptual decision making

Science.gov (United States)

Mostert, Pim; Kok, Peter; de Lange, Floris P.

2015-01-01

A key question within systems neuroscience is how the brain translates physical stimulation into a behavioral response: perceptual decision making. To answer this question, it is important to dissociate the neural activity underlying the encoding of sensory information from the activity underlying the subsequent temporal integration into a decision variable. Here, we adopted a decoding approach to empirically assess this dissociation in human magnetoencephalography recordings. We used a functional localizer to identify the neural signature that reflects sensory-specific processes, and subsequently traced this signature while subjects were engaged in a perceptual decision making task. Our results revealed a temporal dissociation in which sensory processing was limited to an early time window and consistent with occipital areas, whereas decision-related processing became increasingly pronounced over time, and involved parietal and frontal areas. We found that the sensory processing accurately reflected the physical stimulus, irrespective of the eventual decision. Moreover, the sensory representation was stable and maintained over time when it was required for a subsequent decision, but unstable and variable over time when it was task-irrelevant. In contrast, decision-related activity displayed long-lasting sustained components. Together, our approach dissects neuro-anatomically and functionally distinct contributions to perceptual decisions. PMID:26666393

Structure-from-motion: dissociating perception, neural persistence, and sensory memory of illusory depth and illusory rotation.

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Pastukhov, Alexander; Braun, Jochen

2013-02-01

In the structure-from-motion paradigm, physical motion on a screen produces the vivid illusion of an object rotating in depth. Here, we show how to dissociate illusory depth and illusory rotation in a structure-from-motion stimulus using a rotationally asymmetric shape and reversals of physical motion. Reversals of physical motion create a conflict between the original illusory states and the new physical motion: Either illusory depth remains constant and illusory rotation reverses, or illusory rotation stays the same and illusory depth reverses. When physical motion reverses after the interruption in presentation, we find that illusory rotation tends to remain constant for long blank durations (T (blank) ≥ 0.5 s), but illusory depth is stabilized if interruptions are short (T (blank) ≤ 0.1 s). The stability of illusory depth over brief interruptions is consistent with the effect of neural persistence. When this is curtailed using a mask, stability of ambiguous vision (for either illusory depth or illusory rotation) is disrupted. We also examined the selectivity of the neural persistence of illusory depth. We found that it relies on a static representation of an interpolated illusory object, since changes to low-level display properties had little detrimental effect. We discuss our findings with respect to other types of history dependence in multistable displays (sensory stabilization memory, neural fatigue, etc.). Our results suggest that when brief interruptions are used during the presentation of multistable displays, switches in perception are likely to rely on the same neural mechanisms as spontaneous switches, rather than switches due to the initial percept choice at the stimulus onset.

Dissociated neural basis of two behavioral hallmarks of holistic face processing: The whole-part effect and composite-face effect.

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Li, Jin; Huang, Lijie; Song, Yiying; Liu, Jia

2017-07-28

It has been long proposed that our extraordinary face recognition ability stems from holistic face processing. Two widely-used behavioral hallmarks of holistic face processing are the whole-part effect (WPE) and composite-face effect (CFE). However, it remains unknown whether these two effects reflect similar or different aspects of holistic face processing. Here we investigated this question by examining whether the WPE and CFE involved shared or distinct neural substrates in a large sample of participants (N=200). We found that the WPE and CFE showed hemispheric dissociation in the fusiform face area (FFA), that is, the WPE was correlated with face selectivity in the left FFA, while the CFE was correlated with face selectivity in the right FFA. Further, the correlation between the WPE and face selectivity was largely driven by the FFA response to faces, whereas the association between the CFE and face selectivity resulted from suppressed response to objects in the right FFA. Finally, we also observed dissociated correlation patterns of the WPE and CFE in other face-selective regions and across the whole brain. These results suggest that the WPE and CFE may reflect different aspects of holistic face processing, which shed new light on the behavioral dissociations of these two effects demonstrated in literature. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dissociative states in dreams and brain chaos: Implications for creative awareness

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Petr eBob

2015-09-01

Full Text Available This article reviews recent findings indicating some common brain processes during dissociative states and dreaming with the aim to outline a perspective that neural chaotic states during dreaming can be closely related to dissociative states that may manifest in dreams scenery. These data are in agreement with various clinical findings that dissociated states can be projected into the dream scenery in REM sleep periods and dreams may represent their specific interactions that may uncover unusual psychological potential of creativity in psychotherapy, art and scientific discoveries.

Perception, action, and Roelofs effect: a mere illusion of dissociation.

Directory of Open Access Journals (Sweden)

Paul Dassonville

2004-11-01

Full Text Available A prominent and influential hypothesis of vision suggests the existence of two separate visual systems within the brain, one creating our perception of the world and another guiding our actions within it. The induced Roelofs effect has been described as providing strong evidence for this perception/action dissociation: When a small visual target is surrounded by a large frame positioned so that the frame's center is offset from the observer's midline, the perceived location of the target is shifted in the direction opposite the frame's offset. In spite of this perceptual mislocalization, however, the observer can accurately guide movements to the target location. Thus, perception is prone to the illusion while actions seem immune. Here we demonstrate that the Roelofs illusion is caused by a frame-induced transient distortion of the observer's apparent midline. We further demonstrate that actions guided to targets within this same distorted egocentric reference frame are fully expected to be accurate, since the errors of target localization will exactly cancel the errors of motor guidance. These findings provide a mechanistic explanation for the various perceptual and motor effects of the induced Roelofs illusion without requiring the existence of separate neural systems for perception and action. Given this, the behavioral dissociation that accompanies the Roelofs effect cannot be considered evidence of a dissociation of perception and action. This indicates a general need to re-evaluate the broad class of evidence purported to support this hypothesized dissociation.

The neural system of metacognition accompanying decision-making in the prefrontal cortex

Science.gov (United States)

Qiu, Lirong; Su, Jie; Ni, Yinmei; Bai, Yang; Zhang, Xuesong; Li, Xiaoli

2018-01-01

Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. PMID:29684004

Dissociated neural processing for decisions in managers and non-managers.

Science.gov (United States)

Caspers, Svenja; Heim, Stefan; Lucas, Marc G; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl

2012-01-01

Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.

Opposite brain emotion-regulation patterns in identity states of dissociative identity disorder : A PET study and neurobiological model

NARCIS (Netherlands)

Reinders, Antje A. T. S.; Willemsen, Antoon T. M.; den Boer, Johan A.; Vos, Herry P. J.; Veltman, Dick J.; Loewenstein, Richard J.

2014-01-01

Imaging studies in posttraumatic stress disorder (PTSD) have shown differing neural network patterns between hypo-aroused/dissociative and hyper-aroused subtypes. Since dissociative identity disorder (DID) involves different emotional states, this study tests whether DID fits aspects of the

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...

Dissociation of neural correlates of verbal and non-verbal visual working memory with different delays

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Endestad Tor

2007-10-01

Full Text Available Abstract Background Dorsolateral prefrontal cortex (DLPFC, posterior parietal cortex, and regions in the occipital cortex have been identified as neural sites for visual working memory (WM. The exact involvement of the DLPFC in verbal and non-verbal working memory processes, and how these processes depend on the time-span for retention, remains disputed. Methods We used functional MRI to explore the neural correlates of the delayed discrimination of Gabor stimuli differing in orientation. Twelve subjects were instructed to code the relative orientation either verbally or non-verbally with memory delays of short (2 s or long (8 s duration. Results Blood-oxygen level dependent (BOLD 3-Tesla fMRI revealed significantly more activity for the short verbal condition compared to the short non-verbal condition in bilateral superior temporal gyrus, insula and supramarginal gyrus. Activity in the long verbal condition was greater than in the long non-verbal condition in left language-associated areas (STG and bilateral posterior parietal areas, including precuneus. Interestingly, right DLPFC and bilateral superior frontal gyrus was more active in the non-verbal long delay condition than in the long verbal condition. Conclusion The results point to a dissociation between the cortical sites involved in verbal and non-verbal WM for long and short delays. Right DLPFC seems to be engaged in non-verbal WM tasks especially for long delays. Furthermore, the results indicate that even slightly different memory maintenance intervals engage largely differing networks and that this novel finding may explain differing results in previous verbal/non-verbal WM studies.

Dissociated neural processing for decisions in managers and non-managers.

Directory of Open Access Journals (Sweden)

Svenja Caspers

Full Text Available Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.

Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

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Mitani, S; Okamoto, H

1991-05-01

Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

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.

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.

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.

Memory modulation across neural systems: intra-amygdala glucose reverses deficits caused by intraseptal morphine on a spatial task but not on an aversive task.

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McNay, E C; Gold, P E

1998-05-15

Based largely on dissociations of the effects of different lesions on learning and memory, memories for different attributes appear to be organized in independent neural systems. Results obtained with direct injections of drugs into one brain region at a time support a similar conclusion. The present experiments investigated the effects of simultaneous pharmacological manipulation of two neural systems, the amygdala and the septohippocampal system, to examine possible interactions of memory modulation across systems. Morphine injected into the medial septum impaired memory both for avoidance training and during spontaneous alternation. When glucose was concomitantly administered to the amygdala, glucose reversed the morphine-induced deficits in memory during alternation but not for avoidance training. These results suggest that the amygdala is involved in modulation of spatial memory processes and that direct injections of memory-modulating drugs into the amygdala do not always modulate memory for aversive events. These findings are contrary to predictions from the findings of lesion studies and of studies using direct injections of drugs into single brain areas. Thus, the independence of neural systems responsible for processing different classes of memory is less clear than implied by studies using lesions or injections of drugs into single brain areas.

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

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

2015-09-15

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

Enteric neurospheres are not specific to neural crest cultures : Implications for neural stem cell therapies

NARCIS (Netherlands)

Binder, E. (Ellen); D. Natarajan (Dipa); J.E. Cooper (Julie E.); Kronfli, R. (Rania); Cananzi, M. (Mara); J.-M. Delalande (Jean-Marie); C. Mccann; A.J. Burns (Alan); N. Thapar (Nikhil)

2015-01-01

textabstractObjectives Enteric neural stem cells provide hope of curative treatment for enteric neuropathies. Current protocols for their harvesting from humans focus on the generation of 'neurospheres' from cultures of dissociated gut tissue. The study aims to better understand the derivation,

The Dissociative Subtype of Post-traumatic Stress Disorder: Research Update on Clinical and Neurobiological Features.

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van Huijstee, Jytte; Vermetten, Eric

2017-10-21

Recently, a dissociative subtype of post-traumatic stress disorder (PTSD) has been included in the DSM-5. This review focuses on the clinical and neurobiological features that distinguish the dissociative subtype of PTSD from non-dissociative PTSD. Clinically, the dissociative subtype of PTSD is associated with high PTSD severity, predominance of derealization and depersonalization symptoms, a more significant history of early life trauma, and higher levels of comorbid psychiatric disorders. Furthermore, PTSD patients with dissociative symptoms exhibit different psychophysiological and neural responses to the recall of traumatic memories. While individuals with non-dissociative PTSD exhibit an increased heart rate, decreased activation of prefrontal regions, and increased activation of the amygdala in response to traumatic reminders, individuals with the dissociative subtype of PTSD show an opposite pattern. It has been proposed that dissociation is a regulatory strategy to restrain extreme arousal in PTSD through hyperinhibition of limbic regions. In this research update, promises and pitfalls in current research studies on the dissociative subtype of PTSD are listed. Inclusion of the dissociative subtype of PTSD in the DSM-5 stimulates research on the prevalence, symptomatology, and neurobiology of the dissociative subtype of PTSD and poses a challenge to improve treatment outcome in PTSD patients with dissociative symptoms.

Spatiotemporal dissociation of brain activity underlying threat and reward in social anxiety disorder.

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A Richey, John; Ghane, Merage; Valdespino, Andrew; Coffman, Marika C; Strege, Marlene V; White, Susan W; Ollendick, Thomas H

2017-01-01

Social anxiety disorder (SAD) involves abnormalities in social motivation, which may be independent of well-documented differences in fear and arousal systems. Yet, the neurobiology underlying motivational difficulties in SAD is not well understood. The aim of the current study was to spatiotemporally dissociate reward circuitry dysfunction from alterations in fear and arousal-related neural activity during anticipation and notification of social and non-social reward and punishment. During fMRI acquisition, non-depressed adults with social anxiety disorder (SAD; N = 21) and age-, sex- and IQ-matched control subjects (N = 22) completed eight runs of an incentive delay task, alternating between social and monetary outcomes and interleaved in alternating order between gain and loss outcomes. Adults with SAD demonstrated significantly reduced neural activity in ventral striatum during the anticipation of positive but not negative social outcomes. No differences between the SAD and control groups were observed during anticipation of monetary gain or loss outcomes or during anticipation of negative social images. However, consistent with previous work, the SAD group demonstrated amygdala hyper-activity upon notification of negative social outcomes. Degraded anticipatory processing in bilateral ventral striatum in SAD was constrained exclusively to anticipation of positive social information and dissociable from the effects of negative social outcomes previously observed in the amygdala. Alterations in anticipation-related neural signals may represent a promising target for treatment that is not addressed by available evidence-based interventions, which focus primarily on fear extinction and habituation processes. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Dissociable brain systems mediate vicarious learning of stimulus-response and action-outcome contingencies.

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Liljeholm, Mimi; Molloy, Ciara J; O'Doherty, John P

2012-07-18

Two distinct strategies have been suggested to support action selection in humans and other animals on the basis of experiential learning: a goal-directed strategy that generates decisions based on the value and causal antecedents of action outcomes, and a habitual strategy that relies on the automatic elicitation of actions by environmental stimuli. In the present study, we investigated whether a similar dichotomy exists for actions that are acquired vicariously, through observation of other individuals rather than through direct experience, and assessed whether these strategies are mediated by distinct brain regions. We scanned participants with functional magnetic resonance imaging while they performed an observational learning task designed to encourage either goal-directed encoding of the consequences of observed actions, or a mapping of observed actions to conditional discriminative cues. Activity in different parts of the action observation network discriminated between the two conditions during observational learning and correlated with the degree of insensitivity to outcome devaluation in subsequent performance. Our findings suggest that, in striking parallel to experiential learning, neural systems mediating the observational acquisition of actions may be dissociated into distinct components: a goal-directed, outcome-sensitive component and a less flexible stimulus-response component.

Inflight dissociation of zircon in air plasma

Energy Technology Data Exchange (ETDEWEB)

Yugeswaran, S; Selvarajan, V [Bharathiar University, Coimbatore 641046 (India); Ananthapadmanabhan, P V; Thiyagarajan, T K [Laser and Plasma Technology Division, Bhabha Atomic Research Centre, Mumbai - 400 085 (India); Nair, Janardhanan [Ion Arc Technologies Pvt Ltd, Coimbatore (India)

2010-02-01

Thermal dissociation of zircon can be conveniently carried out in a plasma reactor, which is characterized by high temperature, high energy density and high quench rate. Zirconia is recovered from this partially dissociated zircon by alkali leaching. Dissociation of zircon has been conventionally carried out in argon gas, which is expensive. The present paper reports experimental results on thermal dissociation of zircon in air plasma medium. Process simulation for 'inflight' dissociation of zircon in air plasma medium is also presented. The experimental system consists of a central hollow graphite electrode, which acts as the cathode and a graphite anode. The material to be processed is fed centrally through the cathode. The unique feature of the system is that it uses air as the working gas to generate the thermal plasma. The system has been used to study in-flight dissociation of zircon in the thermal plasma jet. Dissociation was carried out over 10-25 kW power range. Results of the study indicate that complete dissociation of zircon to ZrO{sub 2} and silica could be accomplished at 25 kW in air plasma.

Inflight dissociation of zircon in air plasma

International Nuclear Information System (INIS)

Yugeswaran, S; Selvarajan, V; Ananthapadmanabhan, P V; Thiyagarajan, T K; Nair, Janardhanan

2010-01-01

Thermal dissociation of zircon can be conveniently carried out in a plasma reactor, which is characterized by high temperature, high energy density and high quench rate. Zirconia is recovered from this partially dissociated zircon by alkali leaching. Dissociation of zircon has been conventionally carried out in argon gas, which is expensive. The present paper reports experimental results on thermal dissociation of zircon in air plasma medium. Process simulation for 'inflight' dissociation of zircon in air plasma medium is also presented. The experimental system consists of a central hollow graphite electrode, which acts as the cathode and a graphite anode. The material to be processed is fed centrally through the cathode. The unique feature of the system is that it uses air as the working gas to generate the thermal plasma. The system has been used to study in-flight dissociation of zircon in the thermal plasma jet. Dissociation was carried out over 10-25 kW power range. Results of the study indicate that complete dissociation of zircon to ZrO 2 and silica could be accomplished at 25 kW in air plasma.

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.

Neurally-dissociable cognitive components of reading deficits in subacute stroke

Directory of Open Access Journals (Sweden)

Olga eBoukrina

2015-05-01

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

Thermodynamic analysis of the Cu2S-Cu2Te system using dissociation pressure data

International Nuclear Information System (INIS)

Glazov, V.M.; Pashinkin, A.S.; Burkhanov, A.S.; Saleeva, N.M.

1978-01-01

The Knudsen effusive method has been used for studying the dissociation pressure in the Cu 2 S-Cu 2 Te system, and on the basis of the experimental data obtained, the tellurium activity in the system and the mixing energy have been calculated. The dissociation pressure of pure components and alloys containing 10, 30, 50, 70, and 90 mol% of copper telluride within the temperature range of 750-1200 deg C has been studied. A smooth character of the concentration dependence of tellurium activity is observed, which points to the formation of a continuous series of solid solutions in the Cu 2 S-Cu 2 Te system within the temperature range studied. The data on the mixing energy in the system show a good agreement of the values obtained from the dissociation pressure with those determined from the fusibility diagram. The results indicate that the system in question is described well within the framework of the model of regular solutions

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

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.

Dysregulation in cortical reactivity to emotional faces in PTSD patients with high dissociation symptoms

Directory of Open Access Journals (Sweden)

Aleksandra Klimova

2013-09-01

Full Text Available Background: Predominant dissociation in posttraumatic stress disorder (PTSD is characterized by restricted affective responses to positive stimuli. To date, no studies have examined neural responses to a range of emotional expressions in PTSD with high dissociative symptoms. Objective: This study tested the hypothesis that PTSD patients with high dissociative symptoms will display increased event-related potential (ERP amplitudes in early components (N1, P1 to threatening faces (angry, fearful, and reduced later ERP amplitudes (Vertex Positive Potential (VPP, P3 to happy faces compared to PTSD patients with low dissociative symptoms. Methods: Thirty-nine civilians with PTSD were classified as high dissociative (n=16 or low dissociative (n=23 according to their responses on the Clinician Administered Dissociative States Scale. ERPs were recorded, whilst participants viewed emotional (happy, angry, fear and neutral facial expressions in a passive viewing task. Results: High dissociative PTSD patients displayed significantly increased N120 amplitude to the majority of facial expressions (neutral, happy, and angry compared to low dissociative PTSD patients under conscious and preconscious conditions. The high dissociative PTSD group had significantly reduced VPP amplitude to happy faces in the conscious condition. Conclusion: High dissociative PTSD patients displayed increased early (preconscious cortical responses to emotional stimuli, and specific reductions to happy facial expressions in later (conscious, face-specific components compared to low dissociative PTSD patients. Dissociation in PTSD may act to increase initial pre-attentive processing of affective stimuli, and specifically reduce cortical reactivity to happy faces when consciously processing these stimuli.

Prediction of acid dissociation constants of organic compounds using group contribution methods

DEFF Research Database (Denmark)

Zhou, Teng; Jhamb, Spardha; Liang, Xiaodong

2018-01-01

data-points with average absolute error of 0.23; (b) a non-linear GC model for organic compounds using 1622 data-points with average absolute error of 1.18; (c) an artificial neural network (ANN) based GC model for the organic compounds with average absolute error of 0.17. For each of the developed......In this paper, group contribution (GC) property models for the estimation of acid dissociation constants (Ka) of organic compounds are presented. Three GC models are developed to predict the negative logarithm of the acid dissociation constant pKa: (a) a linear GC model for amino acids using 180...

Dissociation of verbal working memory system components using a delayed serial recall task.

Science.gov (United States)

Chein, J M; Fiez, J A

2001-11-01

Functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of component processes in verbal working memory. Based on behavioral research using manipulations of verbal stimulus type to dissociate storage, rehearsal, and executive components of verbal working memory, we designed a delayed serial recall task requiring subjects to encode, maintain, and overtly recall sets of verbal items for which phonological similarity, articulatory length, and lexical status were manipulated. By using a task with temporally extended trials, we were able to exploit the temporal resolution afforded by fMRI to partially isolate neural contributions to encoding, maintenance, and retrieval stages of task performance. Several regions commonly associated with maintenance, including supplementary motor, premotor, and inferior frontal areas, were found to be active across all three trial stages. Additionally, we found that left inferior frontal and supplementary motor regions showed patterns of stimulus and temporal sensitivity implicating them in distinct aspects of articulatory rehearsal, while no regions showed a pattern of sensitivity consistent with a role in phonological storage. Regional modulation by task difficulty was further investigated as a measure of executive processing. We interpret our findings as they relate to notions about the cognitive architecture underlying verbal working memory performance.

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.

Computational and empirical simulations of selective memory impairments: Converging evidence for a single-system account of memory dissociations.

Science.gov (United States)

Curtis, Evan T; Jamieson, Randall K

2018-04-01

Current theory has divided memory into multiple systems, resulting in a fractionated account of human behaviour. By an alternative perspective, memory is a single system. However, debate over the details of different single-system theories has overshadowed the converging agreement among them, slowing the reunification of memory. Evidence in favour of dividing memory often takes the form of dissociations observed in amnesia, where amnesic patients are impaired on some memory tasks but not others. The dissociations are taken as evidence for separate explicit and implicit memory systems. We argue against this perspective. We simulate two key dissociations between classification and recognition in a computational model of memory, A Theory of Nonanalytic Association. We assume that amnesia reflects a quantitative difference in the quality of encoding. We also present empirical evidence that replicates the dissociations in healthy participants, simulating amnesic behaviour by reducing study time. In both analyses, we successfully reproduce the dissociations. We integrate our computational and empirical successes with the success of alternative models and manipulations and argue that our demonstrations, taken in concert with similar demonstrations with similar models, provide converging evidence for a more general set of single-system analyses that support the conclusion that a wide variety of memory phenomena can be explained by a unified and coherent set of principles.

Dissociation and psychosis in dissociative identity disorder and schizophrenia.

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Laddis, Andreas; Dell, Paul F

2012-01-01

Dissociative symptoms, first-rank symptoms of schizophrenia, and delusions were assessed in 40 schizophrenia patients and 40 dissociative identity disorder (DID) patients with the Multidimensional Inventory of Dissociation (MID). Schizophrenia patients were diagnosed with the Structured Clinical Interview for the DSM-IV Axis I Disorders; DID patients were diagnosed with the Structured Clinical Interview for DSM-IV Dissociative Disorders-Revised. DID patients obtained significantly (a) higher dissociation scores; (b) higher passive-influence scores (first-rank symptoms); and (c) higher scores on scales that measure child voices, angry voices, persecutory voices, voices arguing, and voices commenting. Schizophrenia patients obtained significantly higher delusion scores than did DID patients. What is odd is that the dissociation scores of schizophrenia patients were unrelated to their reports of childhood maltreatment. Multiple regression analyses indicated that 81% of the variance in DID patients' dissociation scores was predicted by the MID's Ego-Alien Experiences Scale, whereas 92% of the variance in schizophrenia patients' dissociation scores was predicted by the MID's Voices Scale. We propose that schizophrenia patients' responses to the MID do not index the same pathology as do the responses of DID patients. We argue that neither phenomenological definitions of dissociation nor the current generation of dissociation instruments (which are uniformly phenomenological in nature) can distinguish between the dissociative phenomena of DID and what we suspect are just the dissociation-like phenomena of schizophrenia.

Dissociative part-dependent resting-state activity in dissociative identity disorder: a controlled FMRI perfusion study.

Science.gov (United States)

Schlumpf, Yolanda R; Reinders, Antje A T S; Nijenhuis, Ellert R S; Luechinger, Roger; van Osch, Matthias J P; Jäncke, Lutz

2014-01-01

In accordance with the Theory of Structural Dissociation of the Personality (TSDP), studies of dissociative identity disorder (DID) have documented that two prototypical dissociative subsystems of the personality, the "Emotional Part" (EP) and the "Apparently Normal Part" (ANP), have different biopsychosocial reactions to supraliminal and subliminal trauma-related cues and that these reactions cannot be mimicked by fantasy prone healthy controls nor by actors. Arterial spin labeling perfusion MRI was used to test the hypotheses that ANP and EP in DID have different perfusion patterns in response to rest instructions, and that perfusion is different in actors who were instructed to simulate ANP and EP. In a follow-up study, regional cerebral blood flow of DID patients was compared with the activation pattern of healthy non-simulating controls. Compared to EP, ANP showed elevated perfusion in bilateral thalamus. Compared to ANP, EP had increased perfusion in the dorsomedial prefrontal cortex, primary somatosensory cortex, and motor-related areas. Perfusion patterns for simulated ANP and EP were different. Fitting their reported role-play strategies, the actors activated brain structures involved in visual mental imagery and empathizing feelings. The follow-up study demonstrated elevated perfusion in the left temporal lobe in DID patients, whereas non-simulating healthy controls had increased activity in areas which mediate the mental construction of past and future episodic events. DID involves dissociative part-dependent resting-state differences. Compared to ANP, EP activated brain structures involved in self-referencing and sensorimotor actions more. Actors had different perfusion patterns compared to genuine ANP and EP. Comparisons of neural activity for individuals with DID and non-DID simulating controls suggest that the resting-state features of ANP and EP in DID are not due to imagination. The findings are consistent with TSDP and inconsistent with the idea

Dissociative part-dependent resting-state activity in dissociative identity disorder: a controlled FMRI perfusion study.

Directory of Open Access Journals (Sweden)

Yolanda R Schlumpf

Full Text Available In accordance with the Theory of Structural Dissociation of the Personality (TSDP, studies of dissociative identity disorder (DID have documented that two prototypical dissociative subsystems of the personality, the "Emotional Part" (EP and the "Apparently Normal Part" (ANP, have different biopsychosocial reactions to supraliminal and subliminal trauma-related cues and that these reactions cannot be mimicked by fantasy prone healthy controls nor by actors.Arterial spin labeling perfusion MRI was used to test the hypotheses that ANP and EP in DID have different perfusion patterns in response to rest instructions, and that perfusion is different in actors who were instructed to simulate ANP and EP. In a follow-up study, regional cerebral blood flow of DID patients was compared with the activation pattern of healthy non-simulating controls.Compared to EP, ANP showed elevated perfusion in bilateral thalamus. Compared to ANP, EP had increased perfusion in the dorsomedial prefrontal cortex, primary somatosensory cortex, and motor-related areas. Perfusion patterns for simulated ANP and EP were different. Fitting their reported role-play strategies, the actors activated brain structures involved in visual mental imagery and empathizing feelings. The follow-up study demonstrated elevated perfusion in the left temporal lobe in DID patients, whereas non-simulating healthy controls had increased activity in areas which mediate the mental construction of past and future episodic events.DID involves dissociative part-dependent resting-state differences. Compared to ANP, EP activated brain structures involved in self-referencing and sensorimotor actions more. Actors had different perfusion patterns compared to genuine ANP and EP. Comparisons of neural activity for individuals with DID and non-DID simulating controls suggest that the resting-state features of ANP and EP in DID are not due to imagination. The findings are consistent with TSDP and inconsistent

The rates measurement of methane hydrate formation and dissociation using micro-drilling system application for gas hydrate exploration

Energy Technology Data Exchange (ETDEWEB)

Bin Dou [Engineering Faculty, China Univ. of Geosciences, Wuhan (China)]|[Inst. of Petroleum Engineering, Technology Univ. of Clausthal (Germany); Reinicke, K.M. [Inst. of Petroleum Engineering, Technology Univ. of Clausthal (Germany); Guosheng Jiang; Xiang Wu; Fulong Ning [Engineering Faculty, China Univ. of Geosciences, Wuhan (China)

2006-07-01

When drilling through gas hydrate bearing formations, the energy supplied by virtue of the drilling process may lead to a destabilization of the hydrates surrounding the wellbore. Therefore, as the number of oil and gas fields being development in deepwater and onshore arctic environments increases, greater emphasis should be placed on quantifying the risks, gas hydrates pose to drilling operations. The qualification of these risks requires a comprehensive understanding of gas hydrate-formation and dissociation as a result of drilling induced processes. To develop the required understanding of gas hydrat formation and dissociation, the authors conducted laboratory experiments by using a micro-drilling system, to study the dissociation rates of methane hydrates contained in a tank reactor. The test facility used is a development of China University of Geosciences. The rates of methane hydrate formation and dissociation in the tank reactor were measured at steady-state conditions at pressures ranging from 0.1 to 25 MPa and temperatures ranging from -5 to 20 C. The experimental results show that the rate of hydrate formation is strongly influenced by the fluid system used to form the hydrates, pressure and temperature, with the influence of the temperature on methane hydrate dissociation being stronger than that of the pressure. Drilling speed, drilling fluids and hydrate dissociation inhibitors were also shown to influence hydrate dissociation rate. The derived results have been used to predict hydrate drilling stability for several drilling fluid systems.

Biogrid--a microfluidic device for large-scale enzyme-free dissociation of stem cell aggregates.

Science.gov (United States)

Wallman, Lars; Åkesson, Elisabet; Ceric, Dario; Andersson, Per Henrik; Day, Kelly; Hovatta, Outi; Falci, Scott; Laurell, Thomas; Sundström, Erik

2011-10-07

Culturing stem cells as free-floating aggregates in suspension facilitates large-scale production of cells in closed systems, for clinical use. To comply with GMP standards, the use of substances such as proteolytic enzymes should be avoided. Instead of enzymatic dissociation, the growing cell aggregates may be mechanically cut at passage, but available methods are not compatible with large-scale cell production and hence translation into the clinic becomes a severe bottle-neck. We have developed the Biogrid device, which consists of an array of micrometerscale knife edges, micro-fabricated in silicon, and a manifold in which the microgrid is placed across the central fluid channel. By connecting one side of the Biogrid to a syringe or a pump and the other side to the cell culture, the culture medium with suspended cell aggregates can be aspirated, forcing the aggregates through the microgrid, and ejected back to the cell culture container. Large aggregates are thereby dissociated into smaller fragments while small aggregates pass through the microgrid unaffected. As proof-of-concept, we demonstrate that the Biogrid device can be successfully used for repeated passage of human neural stem/progenitor cells cultured as so-called neurospheres, as well as for passage of suspension cultures of human embryonic stem cells. We also show that human neural stem/progenitor cells tolerate transient pressure changes far exceeding those that will occur in a fluidic system incorporating the Biogrid microgrids. Thus, by using the Biogrid device it is possible to mechanically passage large quantities of cells in suspension cultures in closed fluidic systems, without the use of proteolytic enzymes.

Correlations in the hadronic double diffractive dissociation

International Nuclear Information System (INIS)

Goldegol, Alexandre.

1991-05-01

A given reaction of double diffractive dissociation is studied based on the three-component Deck Model. The correlations among the diffractive slope, the effective mass of the dissociated particle sub-system and the dissociation angle in the Gottfried-Jackson are studied based in this model. 9 refs, 19 figs

Force and Stress along Simulated Dissociation Pathways of Cucurbituril-Guest Systems.

Science.gov (United States)

Velez-Vega, Camilo; Gilson, Michael K

2012-03-13

The field of host-guest chemistry provides computationally tractable yet informative model systems for biomolecular recognition. We applied molecular dynamics simulations to study the forces and mechanical stresses associated with forced dissociation of aqueous cucurbituril-guest complexes with high binding affinities. First, the unbinding transitions were modeled with constant velocity pulling (steered dynamics) and a soft spring constant, to model atomic force microscopy (AFM) experiments. The computed length-force profiles yield rupture forces in good agreement with available measurements. We also used steered dynamics with high spring constants to generate paths characterized by a tight control over the specified pulling distance; these paths were then equilibrated via umbrella sampling simulations and used to compute time-averaged mechanical stresses along the dissociation pathways. The stress calculations proved to be informative regarding the key interactions determining the length-force profiles and rupture forces. In particular, the unbinding transition of one complex is found to be a stepwise process, which is initially dominated by electrostatic interactions between the guest's ammoniums and the host's carbonyl groups, and subsequently limited by the extraction of the guest's bulky bicyclooctane moiety; the latter step requires some bond stretching at the cucurbituril's extraction portal. Conversely, the dissociation of a second complex with a more slender guest is mainly driven by successive electrostatic interactions between the different guest's ammoniums and the host's carbonyl groups. The calculations also provide information on the origins of thermodynamic irreversibilities in these forced dissociation processes.

Hemispheric association and dissociation of voice and speech information processing in stroke.

Science.gov (United States)

Jones, Anna B; Farrall, Andrew J; Belin, Pascal; Pernet, Cyril R

2015-10-01

As we listen to someone speaking, we extract both linguistic and non-linguistic information. Knowing how these two sets of information are processed in the brain is fundamental for the general understanding of social communication, speech recognition and therapy of language impairments. We investigated the pattern of performances in phoneme versus gender categorization in left and right hemisphere stroke patients, and found an anatomo-functional dissociation in the right frontal cortex, establishing a new syndrome in voice discrimination abilities. In addition, phoneme and gender performances were most often associated than dissociated in the left hemisphere patients, suggesting a common neural underpinnings. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Dissociable attentional and affective circuits in medication-naïve children with attention-deficit/hyperactivity disorder.

Science.gov (United States)

Posner, Jonathan; Rauh, Virginia; Gruber, Allison; Gat, Inbal; Wang, Zhishun; Peterson, Bradley S

2013-07-30

Current neurocognitive models of attention-deficit/hyperactivity disorder (ADHD) suggest that neural circuits involving both attentional and affective processing make independent contributions to the phenomenology of the disorder. However, a clear dissociation of attentional and affective circuits and their behavioral correlates has yet to be shown in medication-naïve children with ADHD. Using resting-state functional connectivity MRI (rs-fcMRI) in a cohort of medication naïve children with (N=22) and without (N=20) ADHD, we demonstrate that children with ADHD have reduced connectivity in two neural circuits: one underlying executive attention (EA) and the other emotional regulation (ER). We also demonstrate a double dissociation between these two neural circuits and their behavioral correlates such that reduced connectivity in the EA circuit correlates with executive attention deficits but not with emotional lability, while on the other hand, reduced connectivity in the ER circuit correlates with emotional lability but not with executive attention deficits. These findings suggest potential avenues for future research such as examining treatment effects on these two neural circuits as well as the potential prognostic and developmental significance of disturbances in one circuit vs the other. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Fighting the Whole System: Dissociative Identity Disorder, Labeling Theory, and Iatrogenic Doubting.

Science.gov (United States)

Floris, Jessica; McPherson, Susan

2015-01-01

This research examines how individuals diagnosed with dissociative identity disorder construe their experiences of being labeled with a contested diagnosis. Semistructured interviews were conducted in the United Kingdom with 5 women and 2 men diagnosed with dissociative identity disorder. A framework analysis was conducted. The analysis identified 2 overarching themes: diagnosis cross-examined and navigating care systems. The diagnosis appeared to be continually assessed by participants for its fit with symptoms, and the doubt among professionals seemed to be unhelpfully reflected in participants' attempts to understand and come to terms with their experiences. The findings are considered in light of labeling theory, the iatrogenic effects of professional doubt, and current debates concerning the reliability and validity of psychiatric diagnostic systems that have been reinvigorated by the publication of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

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

Dissociation - a preliminary contextual model

Directory of Open Access Journals (Sweden)

C Krüger

2007-02-01

Full Text Available Background. The Diagnostic and Statistical Manual of Mental Disorders (DSM system has certain limitations when applied to two South African examples of dissociation, because it is descriptive (non-explanatory and focuses on intrapsychic (non-communal processes. Even the existing Western explanatory models of dissociation fail to accommodate fully the communal aspects of dissociation in our South African context. Objectives and methods. The aim was to explore an expanded perspective on dissociation that does not limit it to an intrapsychic phenomenon, but that accounts for the interrelatedness of individuals within their social context. Auto-ethnography was used. In this article a collective, socially orientated, contextual hermeneutic was applied to two local examples of dissociation. Three existing Western models were expanded along multicontextual, collective lines, for them to be more useful in the pluralistic South African context. Results. This preliminary contextual model of dissociation includes a person’s interpersonal, socio-cultural, and spiritual contexts, in addition to the intrapsychic context. Dissociation is considered to be a normal information-processing tool that maintains balanced, coherent selves-in-society, i.e. individuals connected to each other. In the South African context dissociation appears mostly as a normal phenomenon and seldom as a sign of mental illness. Dissociation is pivotal for the normal construction of individual and communal identities in the face of conflicting sets of information from various contexts. Dissociation may help individuals or communities to survive in a world of conflicting messages, where conflict is often interpersonal/cultural/societal in nature, rather than primarily intrapsychic. Conclusions. This model should be developed and evaluated further. Such evaluation would require suitable new local terminology.

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.

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

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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

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

Wavepacket theory of collisional dissociation in molecules

International Nuclear Information System (INIS)

Kulander, K.

1980-01-01

An explicit integration scheme is used to solve the time dependent Schroedinger equation for wavepackets which model collisions in the collinear H + H 2 system. A realistic LEPS-type potential energy surface is used. Collision energies considered are above the dissociation threshold and probabilities for collision induced dissociation are reported. Also quantum mechanical state-to-state transition probabilities are generated. These results are compared to extensive classical trajectory calculations performed on this same system. The time evolution of the wavepacket densities is studied to understand the dynamics of the collinear collisional dissociation process

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.

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

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

2018-08-01

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

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.

Neural correlates of conventional and harm/welfare-based moral decision-making.

Science.gov (United States)

White, Stuart F; Zhao, Hui; Leong, Kelly Kimiko; Smetana, Judith G; Nucci, Larry P; Blair, R James R

2017-12-01

The degree to which social norms are processed by a unitary system or dissociable systems remains debated. Much research on children's social-cognitive judgments has supported the distinction between "moral" (harm/welfare-based) and "conventional" norms. However, the extent to which these norms are processed by dissociable neural systems remains unclear. To address this issue, 23 healthy participants were scanned with functional magnetic resonance imaging (fMRI) while they rated the wrongness of harm/welfare-based and conventional transgressions and neutral vignettes. Activation significantly greater than the neutral vignette baseline was observed in regions implicated in decision-making regions including rostral/ventral medial frontal, anterior insula and dorsomedial frontal cortices when evaluating both harm/welfare-based and social-conventional transgressions. Greater activation when rating harm/welfare-based relative to social-conventional transgressions was seen through much of ACC and bilateral inferior frontal gyrus. Greater activation was observed in superior temporal gyrus, bilateral middle temporal gyrus, left PCC, and temporal-parietal junction when rating social-conventional transgressions relative to harm/welfare-based transgressions. These data suggest that decisions regarding the wrongness of actions, irrespective of whether they involve care/harm-based or conventional transgressions, recruit regions generally implicated in affect-based decision-making. However, there is neural differentiation between harm/welfare-based and conventional transgressions. This may reflect the particular importance of processing the intent of transgressors of conventional norms and perhaps the greater emotional content or salience of harm/welfare-based transgressions.

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....

Short-term synaptic plasticity and heterogeneity in neural systems

Science.gov (United States)

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

2013-01-01

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

Dissociation of Subtraction and Multiplication in the Right Parietal Cortex: Evidence from Intraoperative Cortical Electrostimulation

Science.gov (United States)

Yu, Xiaodan; Chen, Chuansheng; Pu, Song; Wu, Chenxing; Li, Yongnian; Jiang, Tao; Zhou, Xinlin

2011-01-01

Previous research has consistently shown that the left parietal cortex is critical for numerical processing, but the role of the right parietal lobe has been much less clear. This study used the intraoperative cortical electrical stimulation approach to investigate neural dissociation in the right parietal cortex for subtraction and…

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

Dissociative absorption: An empirically unique, clinically relevant, dissociative factor.

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Soffer-Dudek, Nirit; Lassri, Dana; Soffer-Dudek, Nir; Shahar, Golan

2015-11-01

Research of dissociative absorption has raised two questions: (a) Is absorption a unique dissociative factor within a three-factor structure, or a part of one general dissociative factor? Even when three factors are found, the specificity of the absorption factor is questionable. (b) Is absorption implicated in psychopathology? Although commonly viewed as "non-clinical" dissociation, absorption was recently hypothesized to be specifically associated with obsessive-compulsive symptoms. To address these questions, we conducted exploratory and confirmatory factor analyses on 679 undergraduates. Analyses supported the three-factor model, and a "purified" absorption scale was extracted from the original inclusive absorption factor. The purified scale predicted several psychopathology scales. As hypothesized, absorption was a stronger predictor of obsessive-compulsive symptoms than of general psychopathology. In addition, absorption was the only dissociative scale that longitudinally predicted obsessive-compulsive symptoms. We conclude that absorption is a unique and clinically relevant dissociative tendency that is particularly meaningful to obsessive-compulsive symptoms. Copyright © 2015 Elsevier Inc. All rights reserved.

Dissociative symptoms and dissociative disorder comorbidity in patients with obsessive-compulsive disorder.

Science.gov (United States)

Belli, Hasan; Ural, Cenk; Vardar, Melek Kanarya; Yesılyurt, Sema; Oncu, Fatıh

2012-10-01

The present study attempted to assess the dissociative symptoms and overall dissociative disorder comorbidity in patients with obsessive-compulsive disorder (OCD). In addition, we examined the relationship between the severity of obsessive-compulsive symptoms and dissociative symptoms. All patients admitted for the first time to the psychiatric outpatient unit were included in the study. Seventy-eight patients had been diagnosed as having OCD during the 2-year study period. Patients had to meet the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for OCD. Most (76.9%; n = 60) of the patients were female, and 23.1% (n = 18) of the patients were male. Dissociation Questionnaire was used to measure dissociative symptoms. The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Dissociative Disorders interviews and Yale-Brown Obsessive Compulsive Checklist and Severity Scale were used. Eleven (14%) of the patients with OCD had comorbid dissociative disorder. The most prevalent disorder in our study was dissociative depersonalization disorder. Dissociative amnesia and dissociative identity disorder were common as well. The mean Yale-Brown score was 23.37 ± 7.27 points. Dissociation Questionnaire scores were between 0.40 and 3.87 points, and the mean was 2.23 ± 0.76 points. There was a statistically significant positive correlation between Yale-Brown points and Dissociation Questionnaire points. We conclude that dissociative symptoms among patients with OCD should alert clinicians for the presence of a chronic and complex dissociative disorder. Clinicians may overlook an underlying dissociative process in patients who have severe symptoms of OCD. However, a lack of adequate response to cognitive-behavioral and drug therapy may be a consequence of dissociative process. Copyright © 2012 Elsevier Inc. All rights reserved.

Reduced amygdala reactivity and impaired working memory during dissociation in borderline personality disorder.

Science.gov (United States)

Krause-Utz, Annegret; Winter, Dorina; Schriner, Friederike; Chiu, Chui-De; Lis, Stefanie; Spinhoven, Philip; Bohus, Martin; Schmahl, Christian; Elzinga, Bernet M

2017-05-19

Affective hyper-reactivity and impaired cognitive control of emotional material are core features of borderline personality disorder (BPD). A high percentage of individuals with BPD experience stress-related dissociation, including emotional numbing and memory disruptions. So far little is known about how dissociation influences the neural processing of emotional material in the context of a working memory task in BPD. We aimed to investigate whole-brain activity and amygdala functional connectivity (FC) during an Emotional Working Memory Task (EWMT) after dissociation induction in un-medicated BPD patients compared to healthy controls (HC). Using script-driven imagery, dissociation was induced in 17 patients ('BPD_D'), while 12 patients ('BPD_N') and 18 HC were exposed to neutral scripts during fMRI. Afterwards, participants performed the EWMT with neutral vs. negative IAPS pictures vs. no distractors. Main outcome measures were behavioral performance (reaction times, errors) and whole-brain activity during the EWMT. Psychophysiological interaction analysis was used to examine amygdala connectivity during emotional distraction. BPD patients after dissociation induction showed overall WM impairments, a deactivation in bilateral amygdala, and lower activity in left cuneus, lingual gyrus, and posterior cingulate than BPD_N, along with stronger left inferior frontal gyrus activity than HC. Furthermore, reduced amygdala FC with fusiform gyrus and stronger amygdala FC with right middle/superior temporal gyrus and left inferior parietal lobule was observed in BPD_D. Findings suggest that dissociation affects reactivity to emotionally salient material and WM. Altered activity in areas associated with emotion processing, memory, and self-referential processes may contribute to dissociative states in BPD.

A functional dissociation of conflict processing within anterior cingulate cortex

OpenAIRE

Chobok Kim; James Kroger; Jeounghoon Kim

2008-01-01

Goal-directed behavior requires cognitive control to regulate neural processing when conflict is encountered. The dorsal anterior cingulate cortex (dACC) has been associated with detecting response conflict during conflict tasks. However, recent findings have indicated not only that two distinct subregions of dACC are involved in conflict processing but also that the conflict occurs at both perceptual and response levels. We clarified a functional dissociation of the caudal dACC (cdACC) and t...

Dissociating the neural correlates of intra-item and inter-item working-memory binding.

Directory of Open Access Journals (Sweden)

Carinne Piekema

Full Text Available BACKGROUND: Integration of information streams into a unitary representation is an important task of our cognitive system. Within working memory, the medial temporal lobe (MTL has been conceptually linked to the maintenance of bound representations. In a previous fMRI study, we have shown that the MTL is indeed more active during working-memory maintenance of spatial associations as compared to non-spatial associations or single items. There are two explanations for this result, the mere presence of the spatial component activates the MTL, or the MTL is recruited to bind associations between neurally non-overlapping representations. METHODOLOGY/PRINCIPAL FINDINGS: The current fMRI study investigates this issue further by directly comparing intrinsic intra-item binding (object/colour, extrinsic intra-item binding (object/location, and inter-item binding (object/object. The three binding conditions resulted in differential activation of brain regions. Specifically, we show that the MTL is important for establishing extrinsic intra-item associations and inter-item associations, in line with the notion that binding of information processed in different brain regions depends on the MTL. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that different forms of working-memory binding rely on specific neural structures. In addition, these results extend previous reports indicating that the MTL is implicated in working-memory maintenance, challenging the classic distinction between short-term and long-term memory systems.

Perceptual priming versus explicit memory: dissociable neural correlates at encoding.

Science.gov (United States)

Schott, Björn; Richardson-Klavehn, Alan; Heinze, Hans-Jochen; Düzel, Emrah

2002-05-15

We addressed the hypothesis that perceptual priming and explicit memory have distinct neural correlates at encoding. Event-related potentials (ERPs) were recorded while participants studied visually presented words at deep versus shallow levels of processing (LOPs). The ERPs were sorted by whether or not participants later used studied words as completions to three-letter word stems in an intentional memory test, and by whether or not they indicated that these completions were remembered from the study list. Study trials from which words were later used and not remembered (primed trials) and study trials from which words were later used and remembered (remembered trials) were compared to study trials from which words were later not used (forgotten trials), in order to measure the ERP difference associated with later memory (DM effect). Primed trials involved an early (200-450 msec) centroparietal negative-going DM effect. Remembered trials involved a late (900-1200 msec) right frontal, positive-going DM effect regardless of LOP, as well as an earlier (600-800 msec) central, positive-going DM effect during shallow study processing only. All three DM effects differed topographically, and, in terms of their onset or duration, from the extended (600-1200 msec) fronto-central, positive-going shift for deep compared with shallow study processing. The results provide the first clear evidence that perceptual priming and explicit memory have distinct neural correlates at encoding, consistent with Tulving and Schacter's (1990) distinction between brain systems concerned with perceptual representation versus semantic and episodic memory. They also shed additional light on encoding processes associated with later explicit memory, by suggesting that brain processes influenced by LOP set the stage for other, at least partially separable, brain processes that are more directly related to encoding success.

Spiking neural P systems with multiple channels.

Science.gov (United States)

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

2017-11-01

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

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

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

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

Systematics of 2-body diffractive dissociations and search of double diffractive dissociation in K-p interactions at 14.3 GeV/c

International Nuclear Information System (INIS)

Pons, Yvette.

1977-12-01

The diffractive dissociation mechanism is shown to be general when looking at 22 mesonic or baryonic threshold enhancements. The dissociation systems are all produced peripherally and present the property of slope-mass correlation. The production slopes and cross-sections mainly depend on the diffractive excitation mass. The comparison of the results with those from the I.S.R. shows that dissociation systems are very similar in their effective mass shape, momentum transfer structure and angular distributions at center-of-mass energies differing by a factor of ten. Evidence for double diffractive dissociation mechanism is found in 2 exclusive reactions at a cross section level of 5-10+-2 μb. The factorisation hypothesis seems well verified [fr

Dissociation in patients with dissociative seizures: relationships with trauma and seizure symptoms.

Science.gov (United States)

Pick, S; Mellers, J D C; Goldstein, L H

2017-05-01

This study aimed to extend the current understanding of dissociative symptoms experienced by patients with dissociative (psychogenic, non-epileptic) seizures (DS), including psychological and somatoform types of symptomatology. An additional aim was to assess possible relationships between dissociation, traumatic experiences, post-traumatic symptoms and seizure manifestations in this group. A total of 40 patients with DS were compared with a healthy control group (n = 43), matched on relevant demographic characteristics. Participants completed several self-report questionnaires, including the Multiscale Dissociation Inventory (MDI), Somatoform Dissociation Questionnaire-20, Traumatic Experiences Checklist and the Post-Traumatic Diagnostic Scale. Measures of seizure symptoms and current emotional distress (Hospital Anxiety and Depression Scale) were also administered. The clinical group reported significantly more psychological and somatoform dissociative symptoms, trauma, perceived impact of trauma, and post-traumatic symptoms than controls. Some dissociative symptoms (i.e. MDI disengagement, MDI depersonalization, MDI derealization, MDI memory disturbance, and somatoform dissociation scores) were elevated even after controlling for emotional distress; MDI depersonalization scores correlated positively with trauma scores while seizure symptoms correlated with MDI depersonalization, derealization and identity dissociation scores. Exploratory analyses indicated that somatoform dissociation specifically mediated the relationship between reported sexual abuse and DS diagnosis, along with depressive symptoms. A range of psychological and somatoform dissociative symptoms, traumatic experiences and post-traumatic symptoms are elevated in patients with DS relative to healthy controls, and seem related to seizure manifestations. Further studies are needed to explore peri-ictal dissociative experiences in more detail.

Different Mechanism Effect between Gas-Solid and Liquid-Solid Interface on the Three-Phase Coexistence Hydrate System Dissociation in Seawater: A Molecular Dynamics Simulation Study

Directory of Open Access Journals (Sweden)

Zhixue Sun

2017-12-01

Full Text Available Almost 98% of methane hydrate is stored in the seawater environment, the study of microscopic mechanism for methane hydrate dissociation on the sea floor is of great significance to the development of hydrate production, involving a three-phase coexistence system of seawater (3.5% NaCl + hydrate + methane gas. The molecular dynamics method is used to simulate the hydrate dissociation process. The dissociation of hydrate system depends on diffusion of methane molecules from partially open cages and a layer by layer breakdown of the closed cages. The presence of liquid or gas phases adjacent to the hydrate has an effect on the rate of hydrate dissociation. At the beginning of dissociation process, hydrate layers that are in contact with liquid phase dissociated faster than layers adjacent to the gas phase. As the dissociation continues, the thickness of water film near the hydrate-liquid interface became larger than the hydrate-gas interface giving more resistance to the hydrate dissociation. Dissociation rate of hydrate layers adjacent to gas phase gradually exceeds the dissociation rate of layers adjacent to the liquid phase. The difficulty of methane diffusion in the hydrate-liquid side also brings about change in dissociation rate.

Human nasal turbinates as a viable source of respiratory epithelial cells using co-culture system versus dispase-dissociation technique.

Science.gov (United States)

Noruddin, Nur Adelina Ahmad; Saim, Aminuddin B; Chua, Kien Hui; Idrus, Ruszymah

2007-12-01

To compare a co-culture system with a conventional dispase-dissociation method for obtaining functional human respiratory epithelial cells from the nasal turbinates for tissue engineering application. Human respiratory epithelial cells were serially passaged using a co-culture system and a conventional dispase-dissociation technique. The growth kinetics and gene expression levels of the cultured respiratory epithelial cells were compared. Four genes were investigated, namely cytokeratin-18, a marker for ciliated and secretory epithelial cells; cytokeratin-14, a marker for basal epithelial cells; MKI67, a proliferation marker; and MUC5B, a marker for mucin secretion. Immunocytochemical analysis was performed using monoclonal antibodies against the high molecular-weight cytokeratin 34 beta E12, cytokeratin 18, and MUC5A to investigate the protein expression from cultured respiratory epithelial cells. Respiratory epithelial cells cultured using both methods maintained polygonal morphology throughout the passages. At passage 1, co-cultured respiratory epithelial showed a 2.6-times higher growth rate compared to conventional dispase dissociation technique, and 7.8 times higher at passage 2. Better basal gene expression was observed by co-cultured respiratory epithelial cells compared to dispase dissociated cells. Immunocytochemical analyses were positive for the respiratory epithelial cells cultured using both techniques. Co-culture system produced superior quality of cultured human respiratory epithelial cells from the nasal turbinates as compared to dispase dissociation technique.

Study of SI engine fueled with methanol vapor and dissociation gas based on exhaust heat dissociating methanol

International Nuclear Information System (INIS)

Fu, Jianqin; Deng, Banglin; Liu, Jingping; Wang, Linjun; Xu, Zhengxin; Yang, Jing; Shu, Gequn

2014-01-01

Highlights: • The full load power decreases successively from gasoline engine, methanol vapor engine to dissociated methanol engine. • Both power and thermal efficiency of dissociated methanol engine can be improved by boosting pressure. • The conversion efficiency of recovered exhaust gas energy is largely influenced by the BMEP. • At the same BMEP, dissociated methanol engine has higher thermal efficiency than methanol vapor engine and gasoline engine. - Abstract: To improve the fuel efficiency of internal combustion (IC) engine and also achieve the goal of direct usage of methanol fuel on IC engine, an approach of exhaust heat dissociating methanol was investigated, which is a kind of method for IC engine exhaust heat recovery (EHR). A bottom cycle system is coupled with the IC engine exhaust system, which uses the exhaust heat to evaporate and dissociate methanol in its catalytic cracker. The methanol dissociation gas (including methanol vapor) is used as the fuel for IC engine. This approach was applied to both naturally aspirated (NA) engine and turbocharged engine, and the engine performance parameters were predicted by the software GT-power under various kinds of operating conditions. The improvement to IC engine performance and the conversion efficiency of recovered exhaust gas energy can be evaluated by comparing the performances of IC engine fueled with various kinds of fuels (or their compositions). Results show that, from gasoline engine, methanol vapor engine to dissociated methanol engine, the full load power decreases successively in the entire speed area due to the declining of volumetric efficiency, while it is contrary in the thermal efficiency at the same brake mean effective pressure (BMEP) level because of the improving of fuel heating value. With the increase of BMEP, the conversion efficiency of recovered exhaust gas energy is promoted. All those results indicate that the approach of exhaust heat dissociating methanol has large

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.

[Dissociative disorders: from Janet to DSM-IV].

Science.gov (United States)

Nakatani, Y

2000-01-01

I reviewed the literature on dissociation and dissociative disorders from Pierre Janet to DSM-IV, and examined the current trends in research. Janet's theory on hysteria is multifaceted, and is based on three psychological models. Based on a hierarchical model, Janet related hysteric symptoms to the activities within the lower strata of mental hierarchy (automatisms psychologiques), which were demonstrably shown in somnambulism. A second model was based on the concept of a psychological system, which was hypothetically composed of ideas, images, feelings, sensations, and movements. According to this model, dissociation of psychological functions was fundamental to the mechanism of hysteria: loss of integration was thought to engender fixed ideas (ideas fixes) and to lead to the development of a system totally isolated from the whole personality system. Janet also attempted to explain various mental disorders using an economic model. He referred to a loss of equilibration between psychological force and psychological tension. Thus, an unexpected emotional experience was conceived to cause a consumption of reserved psychological force, which was in turn followed by exhaustion associated with hysteric symptoms. Whereas most current researchers regard Janet as the first to study psychological trauma as a principal cause of dissociation, I feel it is important to note that he also emphasized the role of stigmata, i.e., permanent traits of hysteric patients, which were represented as a suggestibility and a tendency toward a narrowing of the consciousness field. Discussion about dissociation and its relation to trauma all but disappeared after Janet. However, during the Second World War and post-war period, some psychiatrists began to pay attention to two emerging phenomena: a high incidence of dissociative symptoms such as fugue and amnesia among combatants, and traumatic neurosis frequently observed among ex-inmates of concentration camps. In the 1970s, interest in

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.

Isotope separation by photoselective dissociative electron

International Nuclear Information System (INIS)

Stevens, C.G.

1978-01-01

A method of separating isotopes based on photoselective electron capture dissociation of molecules having an electron capture cross section dependence on the vibrational state of the molecule is described. A molecular isotope source material is irradiated to selectively excite those molecules containing a desired isotope to a predetermined vibrational state having associated therewith an electron capture energy region substantially non-overlapping with the electron capture energy ranges associated with the lowest vibration states of the molecules. The isotope source is also subjected to electrons having an energy corresponding to the non-overlapping electron capture region whereby the selectively excited molecules preferentially capture electrons and dissociate into negative ions and neutrals. The desired isotope may be in the negative ion product or in the neutral product depending upon the mechanism of dissociation of the particular isotope source used. The dissociation product enriched in the desired isotope is then separated from the reaction system by conventional means. Specifically, 235 UF 6 is separated from a UF 6 mixture by selective excitation followed by dissociative electron capture into 235 UF 5 - and F

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

Science.gov (United States)

Grandjean, Bernard; Maier, Marc A

2017-02-01

Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

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

Functional dissociations in top-down control dependent neural repetition priming.

NARCIS (Netherlands)

Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.S.E.

2007-01-01

Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either

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.

Dissociation in mediation

Directory of Open Access Journals (Sweden)

Daniela Muraru

2008-01-01

Full Text Available This paper approaches several texts that are part of the so-called discourse of mediation, adopting a pragma-dialectical perspective of the theory of dissociation. It is an attempt to identify the uses of dissociative patterns, with special emphasis on the indicators of dissociation. The paper investigates the various uses of the concept of dissociation as a discursive technique in the argumentation on the different aspects that are involved in international conflict, such as the discussion of the notion of peace. The purpose is to identify the role of dissociation, as a device strategically used by the mediator to help the parties minimize the disagreement space, and come to a conflict resolution.

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.

Native-language N400 and P600 predict dissociable language-learning abilities in adults

Science.gov (United States)

Qi, Zhenghan; Beach, Sara D.; Finn, Amy S.; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D.E.

2018-01-01

Language learning aptitude during adulthood varies markedly across individuals. An individual’s native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. PMID:27737775

Dissociation between neural and vascular responses to sympathetic stimulation : contribution of local adrenergic receptor function

Science.gov (United States)

Jacob, G.; Costa, F.; Shannon, J.; Robertson, D.; Biaggioni, I.

2000-01-01

Sympathetic activation produced by various stimuli, eg, mental stress or handgrip, evokes regional vascular responses that are often nonhomogeneous. This phenomenon is believed to be the consequence of the recruitment of differential central neural pathways or of a sympathetically mediated vasodilation. The purpose of this study was to determine whether a similar heterogeneous response occurs with cold pressor stimulation and to test the hypothesis that local differences in adrenergic receptor function could be in part responsible for this diversity. In 8 healthy subjects, local norepinephrine spillover and blood flow were measured in arms and legs at baseline and during sympathetic stimulation induced by baroreflex mechanisms (nitroprusside infusion) or cold pressor stimulation. At baseline, legs had higher vascular resistance (27+/-5 versus 17+/-2 U, P=0.05) despite lower norepinephrine spillover (0.28+/-0.04 versus 0.4+/-0.05 mg. min(-1). dL(-1), P=0.03). Norepinephrine spillover increased similarly in both arms and legs during nitroprusside infusion and cold pressor stimulation. On the other hand, during cold stimulation, vascular resistance increased in arms but not in legs (20+/-9% versus -7+/-4%, P=0.03). Increasing doses of isoproterenol and phenylephrine were infused intra-arterially in arms and legs to estimate beta-mediated vasodilation and alpha-induced vasoconstriction, respectively. beta-Mediated vasodilation was significantly lower in legs compared with arms. Thus, we report a dissociation between norepinephrine spillover and vascular responses to cold stress in lower limbs characterized by a paradoxical decrease in local resistance despite increases in sympathetic activity. The differences observed in adrenergic receptor responses cannot explain this phenomenon.

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 Dissociation of Phonological and Visual Attention Span Disorders in Developmental Dyslexia: fMRI Evidence from Two Case Reports

Science.gov (United States)

Peyrin, C.; Lallier, M.; Demonet, J. F.; Pernet, C.; Baciu, M.; Le Bas, J. F.; Valdois, S.

2012-01-01

A dissociation between phonological and visual attention (VA) span disorders has been reported in dyslexic children. This study investigates whether this cognitively-based dissociation has a neurobiological counterpart through the investigation of two cases of developmental dyslexia. LL showed a phonological disorder but preserved VA span whereas…

The co-occurrence of PTSD and dissociation: differentiating severe PTSD from dissociative-PTSD.

Science.gov (United States)

Armour, Cherie; Karstoft, Karen-Inge; Richardson, J Don

2014-08-01

A dissociative-posttraumatic stress disorder (PTSD) subtype has been included in the DSM-5. However, it is not yet clear whether certain socio-demographic characteristics or psychological/clinical constructs such as comorbid psychopathology differentiate between severe PTSD and dissociative-PTSD. The current study investigated the existence of a dissociative-PTSD subtype and explored whether a number of trauma and clinical covariates could differentiate between severe PTSD alone and dissociative-PTSD. The current study utilized a sample of 432 treatment seeking Canadian military veterans. Participants were assessed with the Clinician Administered PTSD Scale (CAPS) and self-report measures of traumatic life events, depression, and anxiety. CAPS severity scores were created reflecting the sum of the frequency and intensity items from each of the 17 PTSD and 3 dissociation items. The CAPS severity scores were used as indicators in a latent profile analysis (LPA) to investigate the existence of a dissociative-PTSD subtype. Subsequently, several covariates were added to the model to explore differences between severe PTSD alone and dissociative-PTSD. The LPA identified five classes: one of which constituted a severe PTSD group (30.5 %), and one of which constituted a dissociative-PTSD group (13.7 %). None of the included, demographic, trauma, or clinical covariates were significantly predictive of membership in the dissociative-PTSD group compared to the severe PTSD group. In conclusion, a significant proportion of individuals report high levels of dissociation alongside their PTSD, which constitutes a dissociative-PTSD subtype. Further investigation is needed to identify which factors may increase or decrease the likelihood of membership in a dissociative-PTSD subtype group compared to a severe PTSD only group.

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.

Dissociating movement from movement timing in the rat primary motor cortex.

Science.gov (United States)

Knudsen, Eric B; Powers, Marissa E; Moxon, Karen A

2014-11-19

Neural encoding of the passage of time to produce temporally precise movements remains an open question. Neurons in several brain regions across different experimental contexts encode estimates of temporal intervals by scaling their activity in proportion to the interval duration. In motor cortex the degree to which this scaled activity relies upon afferent feedback and is guided by motor output remains unclear. Using a neural reward paradigm to dissociate neural activity from motor output before and after complete spinal transection, we show that temporally scaled activity occurs in the rat hindlimb motor cortex in the absence of motor output and after transection. Context-dependent changes in the encoding are plastic, reversible, and re-established following injury. Therefore, in the absence of motor output and despite a loss of afferent feedback, thought necessary for timed movements, the rat motor cortex displays scaled activity during a broad range of temporally demanding tasks similar to that identified in other brain regions. Copyright © 2014 the authors 0270-6474/14/3415576-11$15.00/0.

Dissociation in Psychiatric Disorders: A Meta-Analysis of Studies Using the Dissociative Experiences Scale.

Science.gov (United States)

Lyssenko, Lisa; Schmahl, Christian; Bockhacker, Laura; Vonderlin, Ruben; Bohus, Martin; Kleindienst, Nikolaus

2018-01-01

Dissociation is a complex, ubiquitous construct in psychopathology. Symptoms of dissociation are present in a variety of mental disorders and have been connected to higher burden of illness and poorer treatment response, and not only in disorders with high levels of dissociation. This meta-analysis offers a systematic and evidence-based study of the prevalence and distribution of dissociation, as assessed by the Dissociative Experiences Scale, within different categories of mental disorders, and it updates an earlier meta-analysis. More than 1,900 original publications were screened, and 216 were included in the meta-analysis, comprising 15,219 individuals in 19 diagnostic categories. The largest mean dissociation scores were found in dissociative disorders (mean scores >35), followed by posttraumatic stress disorder, borderline personality disorder, and conversion disorder (mean scores >25). Somatic symptom disorder, substance-related and addictive disorders, feeding and eating disorders, schizophrenia, anxiety disorder, OCD, and most affective disorders also showed mean dissociation scores >15. Bipolar disorders yielded the lowest dissociation scores (mean score, 14.8). The findings underline the importance of careful psychopathological assessment of dissociative symptoms in the entire range of mental disorders.

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...

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

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...

Algorithm for Calculating the Dissociation Constants of Ampholytes in Nonbuffer Systems

Science.gov (United States)

Lysova, S. S.; Skripnikova, T. A.; Zevatskii, Yu. E.

2018-05-01

An algorithm for calculating the dissociation constants of ampholytes in aqueous solutions is developed on the basis of spectrophotometric data in the UV and visible ranges without pH measurements of a medium and without buffer solutions. The proposed algorithm has been experimentally tested for five ampholytes of different strengths. The relative error of measuring dissociation constants is less than 5%.

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.

Mechanical Dissociation of Retinal Neurons with Vibration

Science.gov (United States)

Motomura, Tamami; Hayashida, Yuki; Murayama, Nobuki

The neuromorphic device, which implements the functions of biological neural circuits by means of VLSI technology, has been collecting much attention in the engineering fields in the last decade. Concurrently, progress in neuroscience research has revealed the nonlinear computation in single neuron levels, suggesting that individual neurons are not merely the circuit elements but computational units. Thus, elucidating the properties of neuronal signal processing is thought to be an essential step for developing the next generation of neuromorphic devices. In the present study, we developed a method for dissociating single neurons from specific sublayers of mammalian retinas with using no proteolytic enzymes but rather combining tissue incubation in a low-Ca2+ medium and the vibro-dissociation technique developed for the slices of brains and spinal cords previously. Our method took shorter time of the procedure, and required less elaborated skill, than the conventional enzymatic method did; nevertheless it yielded enough number of the cells available for acute electrophysiological experiments. The isolated retinal neurons were useful for measuring the nonlinear membrane conductances as well as the spike firing properties under the perforated-patch whole-cell configuration. These neurons also enabled us to examine the effects of proteolytic enzymes on the membrane excitability in those cells.

Computational neural network regression model for Host based Intrusion Detection System

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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.

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

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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

No-Report Paradigms: Extracting the True Neural Correlates of Consciousness.

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Tsuchiya, Naotsugu; Wilke, Melanie; Frässle, Stefan; Lamme, Victor A F

2015-12-01

The goal of consciousness research is to reveal the neural basis of phenomenal experience. To study phenomenology, experimenters seem obliged to ask reports from the subjects to ascertain what they experience. However, we argue that the requirement of reports has biased the search for the neural correlates of consciousness over the past decades. More recent studies attempt to dissociate neural activity that gives rise to consciousness from the activity that enables the report; in particular, no-report paradigms have been utilized to study conscious experience in the full absence of any report. We discuss the advantages and disadvantages of report-based and no-report paradigms, and ask how these jointly bring us closer to understanding the true neural basis of consciousness. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

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...

Resonances in dissociative recombination: Trends and patterns

Energy Technology Data Exchange (ETDEWEB)

Orel, A E; Ngassam, V; Royal, J [Department of Applied Science, University of California, Davis (United States); Roos, J B; Larson, A, E-mail: aeorel@ucdavis.ed [Department of Theoretical Chemistry, Royal Institute of Technology, Stockholm (Sweden)

2009-11-15

In dissociative recombination, the kinetic energy of the incident electron is transferred into excitation of the electrons of the target ion and then into kinetic energy of the fragments. In general, this proceeds via a resonance where the electron is temporarily trapped by the ion, leading to efficient energy transfer. The study of dissociative recombination is the study of these resonances, Rydberg states converging to the ground and excited states of the ion. For a number of systems, we have studied the electronic states involved in dissociative recombination, including the ground and excited states of the ion, the resonant states and the bound Rydberg states of the system, by combining electron scattering calculations with multi-reference configuration interaction quantum chemistry calculations. We will report on trends and patterns in these resonance states. We will discuss studies of dissociative recombination of the rare-gas ions, moving down the periodic table from He{sup +}{sub 2} to Ne{sup +}{sub 2} to Ar{sup +}{sub 2}, where the ground electronic state of the ion is constant, but its polarizability increases. We will also present results on isoelectronic polyatomic systems, such as HCO{sup +} and HCNH{sup +}, as well as the effects of changing the electronic structure slightly such as HCN{sup +}/HNC{sup +} and H{sub 2}CO{sup +}.

Distinct neural mechanisms for body form and body motion discriminations

NARCIS (Netherlands)

Vangeneugden, Joris; Peelen, Marius V; Tadin, Duje; Battelli, Lorella

2014-01-01

Actions can be understood based on form cues (e.g., static body posture) as well as motion cues (e.g., gait patterns). A fundamental debate centers on the question of whether the functional and neural mechanisms processing these two types of cues are dissociable. Here, using fMRI, psychophysics, and

Predictors of trait dissociation and peritraumatic dissociation induced via cold pressor.

Science.gov (United States)

Gómez-Pérez, Lydia; López-Martínez, Alicia Eva; Asmundson, Gordon John Glenn

2013-11-30

Understanding which factors predict individual dissociative response during stressful situations is important to clarify the nature of dissociation and the mechanisms associated to its use as a coping strategy. The present study examined (1) whether experiential avoidance (EA), anxiety sensitivity (AS), depressive symptoms, and state anxiety concurrently predicted trait dissociation (TD)-absorption, amnesia, depersonalization, and total TD scores-and laboratory induced dissociation (LID); and (2) whether TD and catastrophizing predicted LID. We also examined whether catastrophizing mediated the relationships between both AS and depressive symptoms and LID. A total of 101 female undergraduate students participated in a cold pressor task, which significantly induced dissociation. Results of hierarchical regression analyses showed that AS at Time 1 (9 months before the experimental session), as well as depressive symptoms and catastrophizing at the time of the experiment (Time 2), predicted LID at Time 2. Depressive symptoms at Time 2 predicted total TD, absorption, and amnesia scores. AS at Time 1 and depressive symptoms at Time 2 predicted depersonalization. AS, depressive symptoms, and catastrophizing seem to facilitate the use of dissociative strategies by healthy individuals, even in response to non-traumatic but discomforting stress. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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.

Three dimensions of dissociative amnesia.

Science.gov (United States)

Dell, Paul F

2013-01-01

Principal axis factor analysis with promax rotation extracted 3 factors from the 42 memory and amnesia items of the Multidimensional Inventory of Dissociation (MID) database (N = 2,569): Discovering Dissociated Actions, Lapses of Recent Memory and Skills, and Gaps in Remote Memory. The 3 factors' shared variance ranged from 36% to 64%. Construed as scales, the 3 factor scales had Cronbach's alpha coefficients of .96, .94, and .93, respectively. The scales correlated strongly with mean Dissociative Experiences Scale scores, mean MID scores, and total scores on the Structured Clinical Interview for DSM-IV Dissociative Disorders-Revised (SCID-D-R). What is interesting is that the 3 amnesia factors exhibited a range of correlations with SCID-D-R Amnesia scores (.52, .63, and .70, respectively), suggesting that the SCID-D-R Amnesia score emphasizes gaps in remote memory over amnesias related to dissociative identity disorder. The 3 amnesia factor scales exhibited a clinically meaningful pattern of significant differences among dissociative identity disorder, dissociative disorder not otherwise specified-1, dissociative amnesia, depersonalization disorder, and nonclinical participants. The 3 amnesia factors may have greater clinical utility for frontline clinicians than (a) amnesia as discussed in the context of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, nosology of the dissociative disorders or (b) P. Janet's (1893/1977 ) 4-fold classification of dissociative amnesia. The author recommends systematic study of the phenomenological differences within specific dissociative symptoms and their differential relationship to specific dissociative disorders.

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.

Studies of neutron dissociation at Fermilab energies

International Nuclear Information System (INIS)

Ferbel, T.

1975-01-01

The latest results obtained in a continuing investigation of neutron dissociation in (pπ - ) systems in neutron--nucleus collisions between 50 and 300 GeV/c are summarized. The nuclear coherent dissociation data are discussed first; then new measurements of total cross sections of neutrons on nuclei in the Fermilab momentum range are presented; finally, neutron dissociation using a hydrogen target is considered, and the hydrogen data are compared with expectations from simple Deck models. A substantial correlation was observed between the mass and the t of the system produced. The spin structure of the pπ - amplitudes at low mass was described surprisingly well by the simple Deck mechanism. The t-channel helicity amplitudes contained comparable contributions from flip and nonflip terms, and the states produced were not restricted to those expected on the basis of the Morrison rule. (19 figures, 2 tables) (U.S.)

Dissociable effects of motivation and expectancy on conflict processing: an fMRI study.

Science.gov (United States)

Soutschek, Alexander; Stelzel, Christine; Paschke, Lena; Walter, Henrik; Schubert, Torsten

2015-02-01

Previous studies suggest that both motivation and task difficulty expectations activate brain regions associated with cognitive control. However, it remains an open question whether motivational and cognitive determinants of control have similar or dissociable impacts on conflict processing on a neural level. The current study tested the effects of motivation and conflict expectancy on activity in regions related to processing of the target and the distractor information. Participants performed a picture-word interference task in which we manipulated the size of performance-dependent monetary rewards (level of motivation) and the ratio of congruent to incongruent trials within a block (level of conflict expectancy). Our results suggest that motivation improves conflict processing by facilitating task-relevant stimulus processing and task difficulty expectations mainly modulate the processing of distractor information. We conclude that motivation and conflict expectancy engage dissociable control strategies during conflict resolution.

Dissociative part-dependent biopsychosocial reactions to backward masked angry and neutral faces: An fMRI study of dissociative identity disorder.

Science.gov (United States)

Schlumpf, Yolanda R; Nijenhuis, Ellert R S; Chalavi, Sima; Weder, Ekaterina V; Zimmermann, Eva; Luechinger, Roger; La Marca, Roberto; Reinders, A A T Simone; Jäncke, Lutz

2013-01-01

The Theory of Structural Dissociation of the Personality (TSDP) proposes that dissociative identity disorder (DID) patients are fixed in traumatic memories as "Emotional Parts" (EP), but mentally avoid these as "Apparently Normal Parts" of the personality (ANP). We tested the hypotheses that ANP and EP have different biopsychosocial reactions to subliminally presented angry and neutral faces, and that actors instructed and motivated to simulate ANP and EP react differently. Women with DID and matched healthy female actors (CON) were as ANP and EP (DIDanp, DIDep, CONanp, CONep) consecutively exposed to masked neutral and angry faces. Their brain activation was monitored using functional magnetic resonance imaging. The black-and-white dotted masks preceding and following the faces each had a centered colored dot, but in a different color. Participants were instructed to immediately press a button after a perceived color change. State anxiety was assessed after each run using the STAI-S. Final statistical analyses were conducted on 11 DID patients and 15 controls for differences in neural activity, and 13 DID patients and 15 controls for differences in behavior and psychometric measures. Differences between ANP and EP in DID patients and between DID and CON in the two dissociative parts of the personality were generally larger for neutral than for angry faces. The longest reaction times (RTs) existed for DIDep when exposed to neutral faces. Compared to DIDanp, DIDep was associated with more activation of the parahippocampal gyrus. Following neutral faces and compared to CONep, DIDep had more activation in the brainstem, face-sensitive regions, and motor-related areas. DIDanp showed a decreased activity all over the brain in the neutral and angry face condition. There were neither significant within differences nor significant between group differences in state anxiety. CON was not able to simulate genuine ANP and EP biopsychosocially. DID patients have dissociative

Dissociative part-dependent biopsychosocial reactions to backward masked angry and neutral faces: An fMRI study of dissociative identity disorder☆

Science.gov (United States)

Schlumpf, Yolanda R.; Nijenhuis, Ellert R.S.; Chalavi, Sima; Weder, Ekaterina V.; Zimmermann, Eva; Luechinger, Roger; La Marca, Roberto; Reinders, A.A.T. Simone; Jäncke, Lutz

2013-01-01

Objective The Theory of Structural Dissociation of the Personality (TSDP) proposes that dissociative identity disorder (DID) patients are fixed in traumatic memories as “Emotional Parts” (EP), but mentally avoid these as “Apparently Normal Parts” of the personality (ANP). We tested the hypotheses that ANP and EP have different biopsychosocial reactions to subliminally presented angry and neutral faces, and that actors instructed and motivated to simulate ANP and EP react differently. Methods Women with DID and matched healthy female actors (CON) were as ANP and EP (DIDanp, DIDep, CONanp, CONep) consecutively exposed to masked neutral and angry faces. Their brain activation was monitored using functional magnetic resonance imaging. The black-and-white dotted masks preceding and following the faces each had a centered colored dot, but in a different color. Participants were instructed to immediately press a button after a perceived color change. State anxiety was assessed after each run using the STAI-S. Final statistical analyses were conducted on 11 DID patients and 15 controls for differences in neural activity, and 13 DID patients and 15 controls for differences in behavior and psychometric measures. Results Differences between ANP and EP in DID patients and between DID and CON in the two dissociative parts of the personality were generally larger for neutral than for angry faces. The longest reaction times (RTs) existed for DIDep when exposed to neutral faces. Compared to DIDanp, DIDep was associated with more activation of the parahippocampal gyrus. Following neutral faces and compared to CONep, DIDep had more activation in the brainstem, face-sensitive regions, and motor-related areas. DIDanp showed a decreased activity all over the brain in the neutral and angry face condition. There were neither significant within differences nor significant between group differences in state anxiety. CON was not able to simulate genuine ANP and EP biopsychosocially

Native-language N400 and P600 predict dissociable language-learning abilities in adults.

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Qi, Zhenghan; Beach, Sara D; Finn, Amy S; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D E

2017-04-01

Language learning aptitude during adulthood varies markedly across individuals. An individual's native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. Copyright © 2016 Elsevier Ltd. All rights reserved.

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)

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...

From dissociation to trauma? Individual differences in dissociation as predictor of 'trauma' perception

NARCIS (Netherlands)

Rassin, Eric; van Rootselaar, Anne-Fleur

2006-01-01

In clinical literature, dissociative complaints are generally considered to be the result of traumatic experiences. However, it has been argued that dissociative complaints, in turn, may indulge over-reporting of traumatic experiences. Hence, correlations between dissociation and self-reported

Egocentric virtual maze learning in adult survivors of childhood abuse with dissociative disorders: evidence from functional magnetic resonance imaging.

Science.gov (United States)

Weniger, Godehard; Siemerkus, Jakob; Barke, Antonia; Lange, Claudia; Ruhleder, Mirjana; Sachsse, Ulrich; Schmidt-Samoa, Carsten; Dechent, Peter; Irle, Eva

2013-05-30

Present neuroimaging findings suggest two subtypes of trauma response, one characterized predominantly by hyperarousal and intrusions, and the other primarily by dissociative symptoms. The neural underpinnings of these two subtypes need to be better defined. Fourteen women with childhood abuse and the current diagnosis of dissociative amnesia or dissociative identity disorder but without posttraumatic stress disorder (PTSD) and 14 matched healthy comparison subjects underwent functional magnetic resonance imaging (fMRI) while finding their way in a virtual maze. The virtual maze presented a first-person view (egocentric), lacked any topographical landmarks and could be learned only by using egocentric navigation strategies. Participants with dissociative disorders (DD) were not impaired in learning the virtual maze when compared with controls, and showed a similar, although weaker, pattern of activity changes during egocentric learning when compared with controls. Stronger dissociative disorder severity of participants with DD was related to better virtual maze performance, and to stronger activity increase within the cingulate gyrus and the precuneus. Our results add to the present knowledge of preserved attentional and visuospatial mnemonic functioning in individuals with DD. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Assessment of complex dissociative disorder patients and simulated dissociation in forensic contexts.

Science.gov (United States)

Brand, Bethany L; Webermann, Aliya R; Frankel, A Steven

Few assessors receive training in assessing dissociation and complex dissociative disorders (DDs). Potential differential diagnoses include anxiety, mood, psychotic, substance use, and personality disorders, as well as exaggeration and malingering. Individuals with DDs typically elevate on many clinical and validity scales on psychological tests, yet research indicates that they can be distinguished from DD simulators. Becoming informed about the testing profiles of DD individuals and DD simulators can improve the accuracy of differential diagnoses in forensic settings. In this paper, we first review the testing profiles of individuals with complex DDs and contrast them with DD simulators on assessment measures used in forensic contexts, including the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), Personality Assessment Inventory (PAI), and the Structured Inventory of Reported Symptoms (SIRS), as well as dissociation-specific measures such as the Dissociative Experiences Scale (DES) and Structured Clinical Interview for DSM-IV Dissociative Disorders (SCID-D-R). We then provide recommendations for assessing complex trauma and dissociation through the aforementioned assessments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dissociation between morality and disgust: an event-related potential study.

Science.gov (United States)

Yang, Qun; Li, An; Xiao, Xiao; Zhang, Ye; Tian, Xuehong

2014-10-01

This study explored the neural correlates of morality and disgust, particularly, how the mechanisms that mediate our avoidance of physically disgusting and morally abhorrent behaviors are neurologically dissociated during the time-course of processing. Twelve participants were asked to judge the acceptability of different types of behaviors, which varied in their level of moral wrongness and physical disgust, while event-related potentials (ERPs) were recorded. The main results showed that the two morally wrong conditions elicited greater amplitudes of P300-400 at frontal sites than the neutral condition and the physically disgusting, but not morally wrong, condition. The physically disgusting conditions (with and without moral content) elicited significantly more positive deflections in the 500-600 ms timeframe than the neutral condition at central-posterior sites. These findings indicate that our aversion to harmful substances in the physical environment and offensive behaviors in the social environment may be neurologically dissociable in the temporal dimension. Furthermore, the detection of moral violations may be processed earlier in time than that of physical disgust. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Fast kinetics of calcium dissociation from calsequestrin

Directory of Open Access Journals (Sweden)

MARIANELA BELTRÁN

2006-01-01

Full Text Available We measured the kinetics of calcium dissociation from calsequestrin in solution or forming part of isolated junctional sarcoplasmic reticulum membranes by mixing calsequestrin equilibrated with calcium with calcium-free solutions in a stopped-flow system. In parallel, we measured the kinetics of the intrinsic fluorescence changes that take place following calcium dissociation from calsequestrin. We found that at 25ºC calcium dissociation was 10-fold faster for calsequestrin attached to junctional membranes (k = 109 s-1 than in solution. These results imply that calcium dissociation from calsequestrin in vivo is not rate limiting during excitation-contraction coupling. In addition, we found that the intrinsic fluorescence decrease for calsequestrin in solution or forming part of junctional membranes was significantly slower than the rates of calcium dissociation. The kinetics of intrinsic fluorescence changes had two components for calsequestrin associated to junctional membranes and only one for calsequestrin in solution; the faster component was 8-fold faster (k = 54.1 s-1 than the slower component (k = 6.9 s-1, which had the same k value as for calsequestrin in solution. These combined results suggest that the presence of calsequestrin at high concentrations in a restricted space, such as when bound to the junctional membrane, accelerates calcium dissociation and the resulting structural changes, presumably as a result of cooperative molecular interactions.

Dissociation in small molecules

International Nuclear Information System (INIS)

Dehmer, P.M.

1982-01-01

The study of molecular dissociation processes is one of the most interesting areas of modern spectroscopy owing to the challenges presented bt even the simplest of diatomic molecules. This paper reviews the commonly used descriptions of molecular dissociation processes for diatomic molecules, the selection rules for predissociation, and a few of the principles to be remembered when one is forced to speculate about dissociation mechanisms in a new molecule. Some of these points will be illustrated by the example of dissociative ionization in O 2

Water dissociating on rigid Ni(100): A quantum dynamics study on a full-dimensional potential energy surface

Science.gov (United States)

Liu, Tianhui; Chen, Jun; Zhang, Zhaojun; Shen, Xiangjian; Fu, Bina; Zhang, Dong H.

2018-04-01

We constructed a nine-dimensional (9D) potential energy surface (PES) for the dissociative chemisorption of H2O on a rigid Ni(100) surface using the neural network method based on roughly 110 000 energies obtained from extensive density functional theory (DFT) calculations. The resulting PES is accurate and smooth, based on the small fitting errors and the good agreement between the fitted PES and the direct DFT calculations. Time dependent wave packet calculations also showed that the PES is very well converged with respect to the fitting procedure. The dissociation probabilities of H2O initially in the ground rovibrational state from 9D quantum dynamics calculations are quite different from the site-specific results from the seven-dimensional (7D) calculations, indicating the importance of full-dimensional quantum dynamics to quantitatively characterize this gas-surface reaction. It is found that the validity of the site-averaging approximation with exact potential holds well, where the site-averaging dissociation probability over 15 fixed impact sites obtained from 7D quantum dynamics calculations can accurately approximate the 9D dissociation probability for H2O in the ground rovibrational state.

Childhood Traumatic Experiences, Dissociative Symptoms, and Dissociative Disorder Comorbidity Among Patients With Panic Disorder: A Preliminary Study.

Science.gov (United States)

Ural, Cenk; Belli, Hasan; Akbudak, Mahir; Tabo, Abdulkadir

2015-01-01

This study assessed childhood trauma history, dissociative symptoms, and dissociative disorder comorbidity in patients with panic disorder (PD). A total of 92 psychotropic drug-naive patients with PD, recruited from outpatient clinics in the psychiatry department of a Turkish hospital, were involved in the study. Participants were assessed using the Structured Clinical Interview for DSM-IV Dissociative Disorders (SCID-D), Dissociation Questionnaire, Panic and Agoraphobia Scale, Panic Disorder Severity Scale, and Childhood Trauma Questionnaire. Of the patients with PD, 18 (19%) had a comorbid dissociative disorder diagnosis on screening with the SCID-D. The most prevalent disorders were dissociative disorder not otherwise specified, dissociative amnesia, and depersonalization disorders. Patients with a high degree of dissociation symptoms and dissociative disorder comorbidity had more severe PD than those without (p dissociation and PD. Among all of the subscales, the strongest relationship was with childhood emotional abuse. Logistic regression analysis showed that emotional abuse and severity of PD were independently associated with dissociative disorder. In our study, a significant proportion of the patients with PD had concurrent diagnoses of dissociative disorder. We conclude that the predominance of PD symptoms at admission should not lead the clinician to overlook the underlying dissociative process and associated traumatic experiences among these patients.

Dissociation of the Neural Correlates of Visual and Auditory Contextual Encoding

Science.gov (United States)

Gottlieb, Lauren J.; Uncapher, Melina R.; Rugg, Michael D.

2010-01-01

The present study contrasted the neural correlates of encoding item-context associations according to whether the contextual information was visual or auditory. Subjects (N = 20) underwent fMRI scanning while studying a series of visually presented pictures, each of which co-occurred with either a visually or an auditorily presented name. The task…

Dissociative symptoms and dissociative disorders comorbidity in obsessive compulsive disorder: Symptom screening, diagnostic tools and reflections on treatment

OpenAIRE

Belli, Hasan

2014-01-01

Borderline personality disorder, conversion disorder and obsessive compulsive disorder frequently have dissociative symptoms. The literature has demonstrated that the level of dissociation might be correlated with the severity of obsessive compulsive disorder (OCD) and that those not responding to treatment had high dissociative symptoms. The structured clinical interview for DSM-IV dissociative disorders, dissociation questionnaire, somatoform dissociation questionnaire and dissociative expe...

[Dissociative disorders and affective disorders].

Science.gov (United States)

Montant, J; Adida, M; Belzeaux, R; Cermolacce, M; Pringuey, D; Da Fonseca, D; Azorin, J-M

2014-12-01

The phenomenology of dissociative disorders may be complex and sometimes confusing. We describe here two cases who were initially misdiagnosed. The first case concerned a 61 year-old woman, who was initially diagnosed as an isolated dissociative fugue and was actually suffering from severe major depressive episode. The second case concerned a 55 year-old man, who was suffering from type I bipolar disorder and polyvascular disease, and was initially diagnosed as dissociative fugue in a mooddestabilization context, while it was finally a stroke. Yet dissociative disorders as affective disorder comorbidity are relatively unknown. We made a review on this topic. Dissociative disorders are often studied through psycho-trauma issues. Litterature is rare on affective illness comorbid with dissociative disorders, but highlight the link between bipolar and dissociative disorders. The later comorbidity often refers to an early onset subtype with also comorbid panic and depersonalization-derealization disorder. Besides, unipolar patients suffering from dissociative symptoms have more often cyclothymic affective temperament. Despite the limits of such studies dissociative symptoms-BD association seems to correspond to a clinical reality and further works on this topic may be warranted. Copyright © 2014 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

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)

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

The co-occurrence of PTSD and dissociation: differentiating severe PTSD from dissociative-PTSD

DEFF Research Database (Denmark)

Armour, C.; Karstoft, K. I.; Richardson, J. D.

2014-01-01

A dissociative-posttraumatic stress disorder (PTSD) subtype has been included in the DSM-5. However, it is not yet clear whether certain socio-demographic characteristics or psychological/clinical constructs such as comorbid psychopathology differentiate between severe PTSD and dissociative-PTSD....... The current study investigated the existence of a dissociative-PTSD subtype and explored whether a number of trauma and clinical covariates could differentiate between severe PTSD alone and dissociative-PTSD. The current study utilized a sample of 432 treatment seeking Canadian military veterans. Participants...... were assessed with the Clinician Administered PTSD Scale (CAPS) and self-report measures of traumatic life events, depression, and anxiety. CAPS severity scores were created reflecting the sum of the frequency and intensity items from each of the 17 PTSD and 3 dissociation items. The CAPS severity...

Dissociation and Memory Fragmentation in Posttraumatic Stress Disorder: An Evaluation of the Dissociative Encoding Hypothesis

Science.gov (United States)

Bedard-Gilligan, Michele; Zoellner, Lori A.

2012-01-01

Several prominent theories of posttraumatic stress disorder (PTSD) posit that peritraumatic dissociation results in insufficient encoding of the trauma memory and that persistent dissociation prevents memory elaboration, resulting in memory fragmentation and PTSD. In this review, we summarize the empirical literature on peritraumatic and trait dissociation and trauma narrative fragmentation as measured by meta-memory and rater/objective coding. Across 16 studies to date, the association between dissociation and fragmentation was most prominent when examining peritraumatic dissociation and patient's own ratings of memory fragmentation. This relationship did not hold when examining trait dissociation or rater-coded or computer-generated measures of fragmentation. Thus, initial evidence points more toward a strong self-reported association between constructs that is not supported on more objective fragmentation coding. Measurement overlap, construct ambiguity, and exclusion of potential confounds may underlie lack of a strong association between dissociation and objective-rated fragmentation. PMID:22348400

[Screening for major dissociative disorders with the FDS, the German version of the Dissociative Experience Scale].

Science.gov (United States)

Rodewald, Frauke; Gast, Ursula; Emrich, Hinderk M

2006-06-01

The prevalence of major dissociative disorders (dissociative identity disorder, DID and similar forms of dissociative disorder not otherwise specified, DDNOS) in clinical samples is about 5 %. Despite their frequency, major dissociative disorders are often overseen for a long time. Screening-scales have proved to be effective to support clinical diagnosis. The aim of this study was to test, whether the Fragebogen für dissoziative Symptome (FDS), the German version of the Dissociative Experiences Scale (DES), differentiates between patients with dissociative disorders, non-dissociative disorders and non-clinical controls. Additionally, an optimal FDS-cutoff for a more detailed differential-diagnostic evaluation of the dissociative symptomatology should be identified. 150 participants with DID (group DID: n = 44), DDNOS (DDNOS: n = 22), posttraumatic disorders (TRAUMA: n = 20), other non-dissociative disorders (non-TRAUMA: n = 34) and non-clinical controls (KG: n = 30) completed the FDS. In the five diagnostic groups, mean values were calculated and compared for the FDS, DES and FDS-20. Via receiver-operating-curves the cutoff-scores, which differentiated best between participants with and without major dissociative disorders, were identified. FDS, DES and FDS-20 differentiate significantly between patients with and without major dissociative disorders. For all scales, there were significant differences between the diagnostic groups, with mean-scores decreasing continuously from the groups DID to DDNOS and TRAUMA. Between the groups non-TRAUMA and KG tendencies were found in the predicted direction. The optimal cutoff-scores to differentiate between participants with and without major dissociative disorders were 13 (FDS/FDS-20) and 15 (DES). Using these cutoff-scores, at least 90 % of the patients with major dissociative disorders could be identified correctly (sensitivity). The specifity of the scales was 0.89 to 0.90. Screening for major dissociative disorders

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...

Does phasic trauma treatment make patients with dissociative identity disorder treatment more dissociative?

Science.gov (United States)

Brand, Bethany; Loewenstein, Richard J

2014-01-01

Proponents of the iatrogenic model of the etiology of dissociative identity disorder (DID) have expressed concern that treatment focused on direct engagement and interaction with dissociated self-states harms DID patients. However, empirical data have shown that this type of DID treatment is beneficial. Analyzing data from the prospective Treatment of Patients With Dissociative Disorders (TOP DD) Study, we test empirically whether DID treatment is associated with clinically adverse manifestations of dissociated self-states: acting so differently that one feels like different people, hearing voices, and dissociative amnesia. We show that, over the course of the study, there were significant decreases in feeling like different people and hearing voices. These results indicate that this form of DID treatment does not lead to symptomatic worsening in these dimensions, as predicted by the iatrogenic model. Indeed, treatment provided by TOP DD therapists reduced, rather than increased, the extent to which patients experienced manifestations of pathological dissociation. Because severe symptomatology and impairment are associated with DID, iatrogenic harm may come from depriving DID patients of treatment that targets DID symptomatology.

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...

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.

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.

Is the dissociative adult suggestible? A test of the trauma and fantasy models of dissociation.

Science.gov (United States)

Kluemper, Nicole S; Dalenberg, Constance

2014-01-01

Psychologists have long assumed a connection between traumatic experience and psychological dissociation. This hypothesis is referred to as the trauma model of dissociation. In the past decade, a series of papers have been published that question this traditional causal link, proposing an alternative fantasy model of dissociation. In the present research, the relationship among dissociation, suggestibility, and fantasy proneness was examined. Suggestibility was measured through the Gudjonsson Scale of Interrogative Suggestibility (GSS) as well as an autobiographically based version of this measure based on the events of September 11, 2001. Consistent with prior research and with the trauma model, dissociation correlated positively with trauma severity (r = .32, p suggestibility measure. Although some participants did become quite emotional during the procedure, the risk/benefit ratio was perceived by almost all participants to be positive, with more reactive individuals evaluating the procedure more positively. The results consistently support the trauma model of dissociation and fail to support the fantasy model of dissociation.

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.

Strong-field dissociation dynamics

International Nuclear Information System (INIS)

DiMauro, L.F.; Yang, Baorui.

1993-01-01

The strong-field dissociation behavior of diatomic molecules is examined under two distinctive physical scenarios. In the first scenario, the dissociation of the isolated hydrogen and deuterium molecular ions is discussed. The dynamics of above-threshold dissociation (ATD) are investigated over a wide range of green and infrared intensities and compared to a dressed-state model. The second situation arises when strong-field neutral dissociation is followed by ionization of the atomic fragments. The study results in a direct measure of the atomic fragment's ac-Stark shift by observing the intensity-dependent shifts in the electron or nuclear fragment kinetic energy. 8 figs., 14 refs

The specialization of function: cognitive and neural perspectives.

Science.gov (United States)

Mahon, Bradford Z; Cantlon, Jessica F

2011-05-01

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

Global functioning and disability in dissociative disorders.

Science.gov (United States)

Mueller-Pfeiffer, Christoph; Rufibach, Kaspar; Perron, Noelle; Wyss, Daniela; Kuenzler, Cornelia; Prezewowsky, Cornelia; Pitman, Roger K; Rufer, Michael

2012-12-30

Dissociative disorders are frequent comorbid conditions of other mental disorders. Yet, there is controversy about their clinical relevance, and little systematic research has been done on how they influence global functioning. Outpatients and day care patients (N=160) of several psychiatric units in Switzerland were assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV Axis I Disorders, Structured Clinical Interview for DSM-IV Dissociative Disorders, Global Assessment of Functioning Scale, and World Health Organization Disability Assessment Schedule-II. The association between subjects with a dissociative disorder (N=30) and functional impairment after accounting for non-dissociative axis I disorders was evaluated by linear regression models. We found a proportion of 18.8% dissociative disorders (dissociative amnesia=0%, dissociative fugue=0.6%, depersonalization disorder=4.4%, dissociative identity disorder=7.5%, dissociative disorder-not-otherwise-specified=6.3%) across treatment settings. Adjusted for other axis I disorders, subjects with a comorbid dissociative identity disorder or dissociative disorder-not-otherwise-specified had a median global assessment of functioning score that was 0.86 and 0.88 times, respectively, the score of subjects without a comorbid dissociative disorder. These findings support the hypothesis that complex dissociative disorders, i.e., dissociative identity disorder and dissociative disorder-not-otherwise-specified, contribute to functional impairment above and beyond the impact of co-existing non-dissociative axis I disorders, and that they qualify as "serious mental illness". Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Dissociation of acetaldehyde in intense laser field: Coulomb explosion or field-assisted dissociation?

Science.gov (United States)

Elshakre, Mohamed E.; Gao, Lirong; Tang, Xiaoping; Wang, Sufan; Shu, Yafei; Kong, Fanao

2003-09-01

Dissociation of acetaldehyde in moderate strong laser field of 1013-1014W/cm2 was investigated. Singly charged parent ion CH3CHO+ and fragmental ions CH3+, CHO+, C2H4+, O+, CH2CHO+, and H+ were produced by 800 nm laser of 100 fs pulse duration and recorded by time-of-flight mass spectrometer. The CH3+ fragment further dissociated to CH2+, CH+, and C+ ions at higher intensity. Ab initio calculated results show that the singly-, doubly-, and triply charged parent ions are stable. So, the dissociation mechanism was not due to Coulomb explosion of multicharged ion. A field-assisted dissociation (FAD) theory, which assumes that only one bond undergoes dissociation while the rest of the molecular geometry stays unchanged, was employed to treat the dissociation dynamics. Accordingly, the dressed potential energy surfaces of the ground state for the parent and the fragment ions were calculated. Corresponding quasiclassical trajectory calculations show that the bond ruptures take place in the order of C-C, C-O, and C-H, agreeing with the observation. The observed angular dependence and charge distribution of the product ions can also be interpreted by the FAD theory.

Dissociative symptoms and dissociative disorders comorbidity in obsessive compulsive disorder: Symptom screening, diagnostic tools and reflections on treatment.

Science.gov (United States)

Belli, Hasan

2014-08-16

Borderline personality disorder, conversion disorder and obsessive compulsive disorder frequently have dissociative symptoms. The literature has demonstrated that the level of dissociation might be correlated with the severity of obsessive compulsive disorder (OCD) and that those not responding to treatment had high dissociative symptoms. The structured clinical interview for DSM-IV dissociative disorders, dissociation questionnaire, somatoform dissociation questionnaire and dissociative experiences scale can be used for screening dissociative symptoms and detecting dissociative disorders in patients with OCD. However, a history of neglect and abuse during childhood is linked to a risk factor in the pathogenesis of dissociative psychopathology in adults. The childhood trauma questionnaire-53 and childhood trauma questionnaire-40 can be used for this purpose. Clinicians should not fail to notice the hidden dissociative symptoms and childhood traumatic experiences in OCD cases with severe symptoms that are resistant to treatment. Symptom screening and diagnostic tools used for this purpose should be known. Knowing how to treat these pathologies in patients who are diagnosed with OCD can be crucial.

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.

Neural networks for feedback feedforward nonlinear control systems.

Science.gov (United States)

Parisini, T; Zoppoli, R

1994-01-01

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

Correlations in the hadronic double diffractive dissociation; Correlacoes na dupla dissociacao difrativa hadronica

Energy Technology Data Exchange (ETDEWEB)

Goldegol, Alexandre

1991-05-01

A given reaction of double diffractive dissociation is studied based on the three-component Deck Model. The correlations among the diffractive slope, the effective mass of the dissociated particle sub-system and the dissociation angle in the Gottfried-Jackson are studied based in this model. 9 refs, 19 figs.

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.

Hyperglycemia associated dissociative fugue (organic dissociative disorder) in an elderly.

Science.gov (United States)

Ram, Dushad; Ashoka, H G; Gowdappa, Basavnna

2015-01-01

Inadequate glycemic control in patients with diabetes is known to be associated with psychiatric disorders such as depression, anxiety disorder, and cognitive impairment. However, dissociative syndrome has not been reported so far. Here we are reporting a case of repeated dissociative fugue associated with hyperglycemia, in an elderly with type II diabetes. Possible neurobiological mechanism has been discussed.

Hyperglycemia associated dissociative fugue (organic dissociative disorder) in an elderly

OpenAIRE

Ram, Dushad; Ashoka, H. G; Gowdappa, Basavnna

2015-01-01

Inadequate glycemic control in patients with diabetes is known to be associated with psychiatric disorders such as depression, anxiety disorder, and cognitive impairment. However, dissociative syndrome has not been reported so far. Here we are reporting a case of repeated dissociative fugue associated with hyperglycemia, in an elderly with type II diabetes. Possible neurobiological mechanism has been discussed.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

Science.gov (United States)

Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

2009-05-28

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.

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.

Psychobiological characteristics of dissociative identity disorder: a symptom provocation study.

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Reinders, A A T Simone; Nijenhuis, Ellert R S; Quak, Jacqueline; Korf, Jakob; Haaksma, Jaap; Paans, Anne M J; Willemsen, Antoon T M; den Boer, Johan A

2006-10-01

Dissociative identity disorder (DID) patients function as two or more identities or dissociative identity states (DIS), categorized as 'neutral identity states' (NIS) and 'traumatic identity states' (TIS). NIS inhibit access to traumatic memories thereby enabling daily life functioning. TIS have access and responses to these memories. We tested whether these DIS show different psychobiological reactions to trauma-related memory. A symptom provocation paradigm with 11 DID patients was used in a two-by-two factorial design setting. Both NIS and TIS were exposed to a neutral and a trauma-related memory script. Three psychobiological parameters were tested: subjective ratings (emotional and sensori-motor), cardiovascular responses (heart rate, blood pressure, heart rate variability) and regional cerebral blood flow as determined with H(2)(15)O positron emission tomography. Psychobiological differences were found for the different DIS. Subjective and cardiovascular reactions revealed significant main and interactions effects. Regional cerebral blood flow data revealed different neural networks to be associated with different processing of the neutral and trauma-related memory script by NIS and TIS. Patients with DID encompass at least two different DIS. These identities involve different subjective reactions, cardiovascular responses and cerebral activation patterns to a trauma-related memory script.

Critical Neural Substrates for Correcting Unexpected Trajectory Errors and Learning from Them

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Mutha, Pratik K.; Sainburg, Robert L.; Haaland, Kathleen Y.

2011-01-01

Our proficiency at any skill is critically dependent on the ability to monitor our performance, correct errors and adapt subsequent movements so that errors are avoided in the future. In this study, we aimed to dissociate the neural substrates critical for correcting unexpected trajectory errors and learning to adapt future movements based on…

Dissociation and memory fragmentation in post-traumatic stress disorder: an evaluation of the dissociative encoding hypothesis.

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Bedard-Gilligan, Michele; Zoellner, Lori A

2012-01-01

Several prominent theories of post-traumatic stress disorder (PTSD) posit that peritraumatic dissociation results in insufficient encoding of the trauma memory and that persistent dissociation prevents memory elaboration, resulting in memory fragmentation and PTSD. In this review we summarise the empirical literature on peritraumatic and trait dissociation and trauma narrative fragmentation as measured by meta-memory and rater/objective coding. Across 16 studies to date, the association between dissociation and fragmentation was most prominent when examining peritraumatic dissociation and patient's own ratings of memory fragmentation. This relationship did not hold when examining trait dissociation or rater-coded or computer-generated measures of fragmentation. Thus initial evidence points more towards a strong self-reported association between constructs that is not supported on more objective fragmentation coding. Measurement overlap, construct ambiguity, and exclusion of potential confounds may underlie lack of a strong association between dissociation and objective-rated fragmentation.

Multiphoton dissociation of polyatomic molecules

International Nuclear Information System (INIS)

Schulz, P.A.

1979-10-01

The dynamics of infrared multiphoton excitation and dissociation of SF 6 was investigated under collision free conditions by a crossed laser-molecular beam method. In order to understand the excitation mechanism and to elucidate the requirements of laser intensity and energy fluence, a series of experiments were carried out to measure the dissociation yield dependences on energy fluence, vibrational temperature of SF 6 , the pulse duration of the CO 2 laser and the frequency in both one and two laser experiments. Translational energy distributions of the SF 5 dissociation product measured by time of flight and angular distributions and the dissociation lifetime of excited SF 6 as inferred from the observation of secondary dissociation of SF 5 into SF 4 and F during the laser pulse suggest that the dynamics of dissociation of excited molecules is dominated by complete energy randomization and rapid intramolecular energy transfer on a nanosecond timescale, and can be adequately described by RRKM theory. An improved phenomenological model including the initial intensity dependent excitation, a rate equation describing the absorption and stimulated emission of single photons, and the unimolecular dissociation of excited molecules is constructed based on available experimental results. The model shows that the energy fluence of the laser determines the excitation of molecules in the quasi-continuum and the excess energy with which molecules dissociate after the laser pulse. The role played by the laser intensity in multiphoton dissociation is more significant than just that of overcoming the intensity dependent absorption in the lowest levels. 63 references

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...

Isotope exchange study of the dissociation of metal-humic complexes

International Nuclear Information System (INIS)

Mizera, J.; Jansova, A.; Hvozdova, I.; Benes, P.

2002-01-01

Prediction of the migration of toxic metals and radionuclides in the environment requires knowledge of equilibrium and kinetic parameters characterising their interaction with humic substance (HS). In this work, isotope exchange of Eu and Co in the systems containing HS has been used to study dissociation of the cations from their complexes with HS under quasi-stationary conditions. In the experimental arrangement of the so-called diaphragm method, a dialysis membrane divides two compartments containing solutions of metal and HS, identical in both half-cells but for radiolabeling ( 152 Eu and 60 Co) applied only in one cell. The membrane is permeable for free metal cation but not for the metal-HS complex. The slow dissociation of metal cation from HS is reflected by retardation (compared to a reference system in the absence of HS) of the rate of the isotope exchange between the two compartments. However, only an apparent dissociation rate can be observed, as detection of fast dissociation is limited by the rate of diffusion of dissociated cations through membrane and by their recombination with available binding sites of HS. The rate of isotope exchange of Eu and Co in the systems with HS (Aldrich sodium humate, soil humic and fulvic acid) was monitored as function of pH (4 and 6), ionic strength (0.01 and 0.1 M), and the degree of HS loading with metal ([M] 0 = 10 -7 - 2x10 -5 M at 10 mg/L HS). For Co, the rate of 60 Co 2+ diffusion through the membrane showed up to control the rate of the isotope exchange indicating that the Co-HS dissociation is too fast to be followed by the diaphragm method, and that the abundance of non-complexed Co is not negligible. The apparent rate of Eu-HS dissociation was found to be enhanced by decreasing pH value, increasing ionic strength, and increasing metal loading (i.e., metal/HS ratio). For interpretation of the experimental kinetic data, a discrete 2-component model (bi-exponential decay function) was applied. Based on

Hyperglycemia associated dissociative fugue (organic dissociative disorder in an elderly

Directory of Open Access Journals (Sweden)

Dushad Ram

2015-01-01

Full Text Available Inadequate glycemic control in patients with diabetes is known to be associated with psychiatric disorders such as depression, anxiety disorder, and cognitive impairment. However, dissociative syndrome has not been reported so far. Here we are reporting a case of repeated dissociative fugue associated with hyperglycemia, in an elderly with type II diabetes. Possible neurobiological mechanism has been discussed.

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

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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.

Dissociative disorders in DSM-5.

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Spiegel, David; Lewis-Fernández, Roberto; Lanius, Ruth; Vermetten, Eric; Simeon, Daphne; Friedman, Matthew

2013-01-01

The rationale, research literature, and proposed changes to the dissociative disorders and conversion disorder in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) are presented. Dissociative identity disorder will include reference to possession as well as identity fragmentation, to make the disorder more applicable to culturally diverse situations. Dissociative amnesia will include dissociative fugue as a subtype, since fugue is a rare disorder that always involves amnesia but does not always include confused wandering or loss of personality identity. Depersonalization disorder will include derealization as well, since the two often co-occur. A dissociative subtype of posttraumatic stress disorder (PTSD), defined by the presence of depersonalization or derealization in addition to other PTSD symptoms, is being recommended, based upon new epidemiological and neuroimaging evidence linking it to an early life history of adversity and a combination of frontal activation and limbic inhibition. Conversion disorder (functional neurological symptom disorder) will likely remain with the somatic symptom disorders, despite considerable dissociative comorbidity.

Dynamics of dissociation versus ionization in strong laser fields

International Nuclear Information System (INIS)

DiMauro, L.F.; Yang, B.

1993-01-01

In this paper, experimental results are presented which clearly demonstrate the effectiveness that an external field has in altering the dissociation dynamics. The experiment examines the strong-field dissociation dynamics of molecular hydrogen ions and its deuterated isotopes. These studies involve multiphoton excitation in the intensity regime of 10 11-14 W/cm 2 with the fundamental and second harmonic of a ND:YAG or ND:YLF laser system. Measurements include energy resolved electron and mass spectroscopy which provide useful probes in elucidating the interaction dynamics predicted by existing models. The example this in this paper, examines the strong-field dissociation of H 2 + , HD + , and D 2 + at green (0.5 μm) and (1μm) frequencies. The diatomic ions are formed via multiphonon ionization of the neutral precursor which is physically separable from the dissociation process. This study provides the first observation of the dynamics associated with the above threshold dissociation (ATD) process and analogies will be made with the more familiar above threshold ionization (ATI) phenomenon

Temporal neural networks and transient analysis of complex engineering systems

Science.gov (United States)

Uluyol, Onder

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

Optical neural network system for pose determination of spinning satellites

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Lee, Andrew; Casasent, David

1990-01-01

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

On the neural mechanisms subserving consciousness and attention

Directory of Open Access Journals (Sweden)

Catherine eTallon-Baudry

2012-01-01

Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

Neural mechanisms of selective attention in the somatosensory system.

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

2016-09-01

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

Catalytic methanol dissociation

International Nuclear Information System (INIS)

Alcinikov, Y.; Fainberg, V.; Garbar, A.; Gutman, M.; Hetsroni, G.; Shindler, Y.; Tatrtakovsky, L.; Zvirin, Y.

1998-01-01

Results of the methanol dissociation study on copper/potassium catalyst with alumina support at various temperatures are presented. The following gaseous and liquid products at. The catalytic methanol dissociation is obtained: hydrogen, carbon monoxide, carbon dioxide, methane, and dimethyl ether. Formation rates of these products are discussed. Activation energies of corresponding reactions are calculated

Synthesis of recurrent neural networks for dynamical system simulation.

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

2016-08-01

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

A modular neural network scheme applied to fault diagnosis in electric power systems.

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Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

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

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Brown, Ramsay A; Swanson, Larry W

2013-09-01

Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

Alpha and theta brain oscillations index dissociable processes in spoken word recognition.

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Strauß, Antje; Kotz, Sonja A; Scharinger, Mathias; Obleser, Jonas

2014-08-15

Slow neural oscillations (~1-15 Hz) are thought to orchestrate the neural processes of spoken language comprehension. However, functional subdivisions within this broad range of frequencies are disputed, with most studies hypothesizing only about single frequency bands. The present study utilizes an established paradigm of spoken word recognition (lexical decision) to test the hypothesis that within the slow neural oscillatory frequency range, distinct functional signatures and cortical networks can be identified at least for theta- (~3-7 Hz) and alpha-frequencies (~8-12 Hz). Listeners performed an auditory lexical decision task on a set of items that formed a word-pseudoword continuum: ranging from (1) real words over (2) ambiguous pseudowords (deviating from real words only in one vowel; comparable to natural mispronunciations in speech) to (3) pseudowords (clearly deviating from real words by randomized syllables). By means of time-frequency analysis and spatial filtering, we observed a dissociation into distinct but simultaneous patterns of alpha power suppression and theta power enhancement. Alpha exhibited a parametric suppression as items increasingly matched real words, in line with lowered functional inhibition in a left-dominant lexical processing network for more word-like input. Simultaneously, theta power in a bilateral fronto-temporal network was selectively enhanced for ambiguous pseudowords only. Thus, enhanced alpha power can neurally 'gate' lexical integration, while enhanced theta power might index functionally more specific ambiguity-resolution processes. To this end, a joint analysis of both frequency bands provides neural evidence for parallel processes in achieving spoken word recognition. Copyright © 2014 Elsevier Inc. All rights reserved.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Dissociative symptoms and neuroendocrine dysregulation in depression.

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Bob, Petr; Fedor-Freybergh, Peter; Jasova, Denisa; Bizik, Gustav; Susta, Marek; Pavlat, Josef; Zima, Tomas; Benakova, Hana; Raboch, Jiri

2008-10-01

Dissociative symptoms are traditionally attributed to psychological stressors that produce dissociated memories related to stressful life events. Dissociative disorders and dissociative symptoms including psychogenic amnesia, fugue, dissociative identity-disorder, depersonalization, derealization and other symptoms or syndromes have been reported as an epidemic psychiatric condition that may be coexistent with various psychiatric diagnoses such as depression, schizophrenia, borderline personality disorder or anxiety disorders. According to recent findings also the somatic components of dissociation may occur and influence brain, autonomic and neuroendocrine functions. At this time there are only few studies examining neuroendocrine response related to dissociative symptoms that suggest significant dysregulation of the hypothalamus-pituitary-adrenal (HPA) axis. The aim of the present study is to perform examination of HPA axis functioning indexed by basal cortisol and prolactin and test their relationship to psychic and somatoform dissociative symptoms. Basal cortisol and prolactin and psychic and somatoform dissociative symptoms were assessed in 40 consecutive inpatients with diagnosis of unipolar depression mean age 43.37 (SD=12.21). The results show that prolactin and cortisol as indices of HPA axis functioning manifest significant relationship to dissociative symptoms. Main results represent highly significant correlations obtained by simple regression between psychic dissociative symptoms (DES) and serum prolactin (R=0.55, p=0.00027), and between somatoform dissociation (SDQ-20) and serum cortisol (R=-0.38, p=0.015). These results indicate relationship between HPA-axis reactivity and dissociative symptoms in unipolar depressive patients that could reflect passive coping behavior and disengagement.

The neural basis of body form and body action agnosia.

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Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

2008-10-23

Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

Dissociative symptomatology in cancer patients

Science.gov (United States)

Civilotti, Cristina; Castelli, Lorys; Binaschi, Luca; Cussino, Martina; Tesio, Valentina; Di Fini, Giulia; Veglia, Fabio; Torta, Riccardo

2015-01-01

Introduction: The utilization of the post-traumatic stress disorder (PTSD) diagnostic spectrum is currently being debated to categorize psychological adjustment in cancer patients. The aims of this study were to: (1) evaluate the presence of cancer-related traumatic dissociative symptomatology in a sample of cancer patients; (2) examine the correlation of cancer-related dissociation and sociodemographic and medical variables, anxiety, depression, and post-traumatic stress symptomatology; (3) investigate the predictors of cancer-related dissociation. Methods: Ninety-two mixed cancer patients (mean age: 58.94, ds = 10.13) recruited from two hospitals in northern Italy were administered a questionnaire on sociodemographic and medical characteristics, the Karnofsky Scale to measure the level of patient activity and medical care requirements, the Hospital Anxiety and Depression Scale (HADS) to evaluate the presence of anxiety and depression, the Impact of Event Scale Revised (IES-R) to assess the severity of intrusion, avoidance, and hypervigilance, and the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) to quantify the traumatic dissociative symptomatology. Results: 31.5% of participants report a PDEQ score above the cutoff. The results indicated that dissociative symptomatology was positively correlated with HADS scores (HADS-Anxiety: r = 0.476, p dissociative symptomatology. The results converged on a three predictor model revealing that IES-R-Intrusion, IES-R-Avoidance, and IES-R-Hyperarousal accounted for 53.9% of the explained variance. Conclusion: These findings allow us to hypothesize a specific psychological reaction which may be ascribed to the traumatic spectrum within the context of cancer, emphasizing the close relationship between the origin of dissociative constituents which, according to the scientific literature, compose the traumatic experience. Our results have implications for understanding dissociative symptomatology in a cancer

Model-independent determination of dissociation energies: method and applications

International Nuclear Information System (INIS)

Vogel, Manuel; Hansen, Klavs; Herlert, Alexander; Schweikhard, Lutz

2003-01-01

A number of methods are available for the purpose of extracting dissociation energies of polyatomic particles. Many of these techniques relate the rate of disintegration at a known excitation energy to the value of the dissociation energy. However, such a determination is susceptible to systematic uncertainties, mainly due to the unknown thermal properties of the particles and the potential existence of 'dark' channels, such as radiative cooling. These problems can be avoided with a recently developed procedure, which applies energy-dependent reactions of the decay products as an uncalibrated thermometer. Thus, it allows a direct measurement of dissociation energies, without any assumption on properties of the system or on details of the disintegration process. The experiments have been performed in a Penning trap, where both rate constants and branching ratios have been measured. The dissociation energies determined with different versions of the method yield identical values, within a small uncertainty

Experimentally-induced dissociation impairs visual memory.

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Brewin, Chris R; Mersaditabari, Niloufar

2013-12-01

Dissociation is a phenomenon common in a number of psychological disorders and has been frequently suggested to impair memory for traumatic events. In this study we explored the effects of dissociation on visual memory. A dissociative state was induced experimentally using a mirror-gazing task and its short-term effects on memory performance were investigated. Sixty healthy individuals took part in the experiment. Induced dissociation impaired visual memory performance relative to a control condition; however, the degree of dissociation was not associated with lower memory scores in the experimental group. The results have theoretical and practical implications for individuals who experience frequent dissociative states such as patients with posttraumatic stress disorder (PTSD). Copyright © 2013 Elsevier Inc. All rights reserved.

Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals

NARCIS (Netherlands)

Fonseca, Andre; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosue

2015-01-01

Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis,

Speech-Language Dissociations, Distractibility, and Childhood Stuttering

Science.gov (United States)

Conture, Edward G.; Walden, Tedra A.; Lambert, Warren E.

2015-01-01

Purpose This study investigated the relation among speech-language dissociations, attentional distractibility, and childhood stuttering. Method Participants were 82 preschool-age children who stutter (CWS) and 120 who do not stutter (CWNS). Correlation-based statistics (Bates, Appelbaum, Salcedo, Saygin, & Pizzamiglio, 2003) identified dissociations across 5 norm-based speech-language subtests. The Behavioral Style Questionnaire Distractibility subscale measured attentional distractibility. Analyses addressed (a) between-groups differences in the number of children exhibiting speech-language dissociations; (b) between-groups distractibility differences; (c) the relation between distractibility and speech-language dissociations; and (d) whether interactions between distractibility and dissociations predicted the frequency of total, stuttered, and nonstuttered disfluencies. Results More preschool-age CWS exhibited speech-language dissociations compared with CWNS, and more boys exhibited dissociations compared with girls. In addition, male CWS were less distractible than female CWS and female CWNS. For CWS, but not CWNS, less distractibility (i.e., greater attention) was associated with more speech-language dissociations. Last, interactions between distractibility and dissociations did not predict speech disfluencies in CWS or CWNS. Conclusions The present findings suggest that for preschool-age CWS, attentional processes are associated with speech-language dissociations. Future investigations are warranted to better understand the directionality of effect of this association (e.g., inefficient attentional processes → speech-language dissociations vs. inefficient attentional processes ← speech-language dissociations). PMID:26126203

Objective documentation of child abuse and dissociation in 12 murderers with dissociative identity disorder.

Science.gov (United States)

Lewis, D O; Yeager, C A; Swica, Y; Pincus, J H; Lewis, M

1997-12-01

The skepticism regarding the existence of dissociative identity disorder as well as the abuse that engenders it persists for lack of objective documentation. This is doubly so for the disorder in murderers because of issues of suspected malingering. This article presents objective verification of both dissociative symptoms and severe abuse during childhood in a series of adult murderers with dissociative identity disorder. This study consisted of a review of the clinical records of 11 men and one woman with DSM-IV-defined dissociative identity disorder who had committed murder. Data were gathered from medical, psychiatric, social service, school, military, and prison records and from records of interviews with subjects' family members and others. Handwriting samples were also examined. Data were analyzed qualitatively. Signs and symptoms of dissociative identity disorder in childhood and adulthood were corroborated independently and from several sources in all 12 cases; objective evidence of severe abuse was obtained in 11 cases. The subjects had amnesia for most of the abuse and underreported it. Marked changes in writing style and/or signatures were documented in 10 cases. This study establishes, once and for all, the linkage between early severe abuse and dissociative identity disorder. Further, the data demonstrate that the disorder can be distinguished from malingering and from other disorders. The study shows that it is possible, with great effort, to obtain objective evidence of both the symptoms of dissociative identity disorder and the abuse that engenders it.

The importance of actions and the worth of an object: dissociable neural systems representing core value and economic value

Science.gov (United States)

Coppin, Géraldine; Schwartz, Sophie; Sander, David

2012-01-01

Neuroeconomic research has delineated neural regions involved in the computation of value, referring to a currency for concrete choices and decisions (‘economic value’). Research in psychology and sociology, on the other hand, uses the term ‘value’ to describe motivational constructs that guide choices and behaviors across situations (‘core value’). As a first step towards an integration of these literatures, we compared the neural regions computing economic value and core value. Replicating previous work, economic value computations activated a network centered on medial orbitofrontal cortex. Core value computations activated medial prefrontal cortex, a region involved in the processing of self-relevant information and dorsal striatum, involved in action selection. Core value ratings correlated with activity in precuneus and anterior prefrontal cortex, potentially reflecting the degree to which a core value is perceived as internalized part of one’s self-concept. Distributed activation pattern in insula and ACC allowed differentiating individual core value types. These patterns may represent evaluation profiles reflecting prototypical fundamental concerns expressed in the core value types. Our findings suggest mechanisms by which core values, as motivationally important long-term goals anchored in the self-schema, may have the behavioral power to drive decisions and behaviors in the absence of immediately rewarding behavioral options. PMID:21642352

Dissociative Part-Dependent Resting-State Activity in Dissociative Identity Disorder : A Controlled fMRI Perfusion Study

NARCIS (Netherlands)

Schlumpf, Yolanda R.; Reinders, Antje A. T. S.; Nijenhuis, Ellert R. S.; Luechinger, Roger; van Osch, Matthias J. P.; Jaencke, Lutz

2014-01-01

Background: In accordance with the Theory of Structural Dissociation of the Personality (TSDP), studies of dissociative identity disorder (DID) have documented that two prototypical dissociative subsystems of the personality, the "Emotional Part'' (EP) and the "Apparently Normal Part'' (ANP), have

Efficient decoding with steady-state Kalman filter in neural interface systems.

Science.gov (United States)

Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

2011-02-01

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

CO2 Dissociation by Low Current Gliding Discharge in the Reverse Vortex Flow

Science.gov (United States)

Gutsol, Alexander

2012-10-01

If performed with high energy efficiency, plasma-chemical dissociation of carbon dioxide can be a way of converting and storing energy when there is an excess of electric energy, for example generated by solar elements of wind turbines. CO2 dissociation with efficiency of up to 90% was reported earlier for low pressure microwave discharge in supersonic flow. A new plasma-chemical system uses a low current gliding discharge in the reverse vortex flow of plasma gas. The system is a development of the Gliding Arc in Tornado reactor. The system was used to study dissociation of CO2 in wide ranges of the following experimental parameters: reactor pressure (15-150 kPa), discharge current (50-500 mA), gas flow rate (3-30 liters per minute), and electrode gap length (1-10 cm). Additionally, the effect of thermal energy recuperation on CO2 dissociation efficiency was tested. Plasma chemical efficiency of CO2 dissociation is very low (about 3%) in a short discharge at low pressures (about 15 kPa) when it is defined by electronic excitation. The highest efficiency (above 40%) was reached at pressures 50-70 kPa in a long discharge with thermal energy recuperation. It means that the process is controlled by thermal dissociation with subsequent effective quenching. Plasma chemical efficiency was determined from the data of chromatographic analysis and oscilloscope electric power integration, and also was checked calorimetrically by the thermal balance of the system.

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

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

Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

Science.gov (United States)

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

2016-11-14

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

Learning in Artificial Neural Systems

Science.gov (United States)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

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

Dissociative, depressive, and PTSD symptom severity as correlates of nonsuicidal self-injury and suicidality in dissociative disorder patients.

Science.gov (United States)

Webermann, Aliya R; Myrick, Amie C; Taylor, Christina L; Chasson, Gregory S; Brand, Bethany L

2016-01-01

The present study investigates whether symptom severity can distinguish patients diagnosed with dissociative identity disorder and dissociative disorder not otherwise specified with a recent history of nonsuicidal self-injury (NSSI) and suicide attempts from those patients without recent self-harm. A total of 241 clinicians reported on recent history of patient NSSI and suicide attempts. Of these clinicians' patients, 221 completed dissociative, depressive, and posttraumatic stress disorder symptomatology measures. Baseline cross-sectional data from a naturalistic and prospective study of dissociative disorder patients receiving community treatment were utilized. Analyses evaluated dissociative, depressive, and posttraumatic stress disorder symptom severity as methods of classifying patients into NSSI and suicide attempt groupings. Results indicated that dissociation severity accurately classified patients into NSSI and suicidality groups, whereas depression severity accurately classified patients into NSSI groups. These findings point to dissociation and depression severity as important correlates of NSSI and suicidality in patients with dissociative disorders and have implications for self-harm prevention and treatment.

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

2006-07-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

International Nuclear Information System (INIS)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

2006-01-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

Compact holographic optical neural network system for real-time pattern recognition

Science.gov (United States)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Dissociation dynamics of methylal

Energy Technology Data Exchange (ETDEWEB)

Beaud, P; Frey, H -M; Gerber, T; Mischler, B; Radi, P P; Tzannis, A -P [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

1999-08-01

The dissociation of methylal is investigated using mass spectrometry, combined with a pyrolytic radical source and femtosecond pump probe experiments. Based on preliminary results two reaction paths of methylal dissociation are proposed and discussed. (author) 4 fig., 3 refs.

Neural network training by Kalman filtering in process system monitoring

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

1996-03-01

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

A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

Directory of Open Access Journals (Sweden)

Agustín Flores

2014-01-01

Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

Neural substrates of reliability-weighted visual-tactile multisensory integration

Directory of Open Access Journals (Sweden)

Michael S Beauchamp

2010-06-01

Full Text Available As sensory systems deteriorate in aging or disease, the brain must relearn the appropriate weights to assign each modality during multisensory integration. Using blood-oxygen level dependent functional magnetic resonance imaging (BOLD fMRI of human subjects, we tested a model for the neural mechanisms of sensory weighting, termed “weighted connections”. This model holds that the connection weights between early and late areas vary depending on the reliability of the modality, independent of the level of early sensory cortex activity. When subjects detected viewed and felt touches to the hand, a network of brain areas was active, including visual areas in lateral occipital cortex, somatosensory areas in inferior parietal lobe, and multisensory areas in the intraparietal sulcus (IPS. In agreement with the weighted connection model, the connection weight measured with structural equation modeling between somatosensory cortex and IPS increased for somatosensory-reliable stimuli, and the connection weight between visual cortex and IPS increased for visual-reliable stimuli. This double dissociation of connection strengths was similar to the pattern of behavioral responses during incongruent multisensory stimulation, suggesting that weighted connections may be a neural mechanism for behavioral reliability weighting.for behavioral reliability weighting.

Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

Directory of Open Access Journals (Sweden)

Ruliang Wang

2012-01-01

Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

Dissociative identity disorder: Medicolegal challenges.

Science.gov (United States)

Farrell, Helen M

2011-01-01

Persons with dissociative identity disorder (DID) often present in the criminal justice system rather than the mental health system and perplex experts in both professions. DID is a controversial diagnosis with important medicolegal implications. Defendants have claimed that they committed serious crimes, including rape or murder, while they were in a dissociated state. Asserting that their alter personality committed the bad act, defendants have pleaded not guilty by reason of insanity (NGRI). In such instances, forensic experts are asked to assess the defendant for DID and provide testimony in court. Debate continues over whether DID truly exists, whether expert testimony should be allowed into evidence, and whether it should exculpate defendants for their criminal acts. This article reviews historical and theoretical perspectives on DID, presents cases that illustrate the legal implications and controversies of raising an insanity defense based on multiple personalities, and examines the role of forensic experts asked to comment on DID with the goal of assisting clinicians in the medicolegal assessment of DID in relation to crimes.

Ultrafast dissociation: An unexpected tool for probing molecular dynamics

International Nuclear Information System (INIS)

Morin, Paul; Miron, Catalin

2012-01-01

Highlights: ► Ultrafast dissociation has been investigated by means of XPS and mass spectrometry. ► The interplay between electron relaxation and molecular dynamics is evidenced. ► Extension toward polyatomics, clusters, adsorbed molecules is considered. ► Quantum effects (spectral hole, angular effects) evidence the molecular field anisotropy. -- Abstract: Ultrafast dissociation following core–shell excitation into an antibonding orbital led to the early observation in HBr of atomic Auger lines associated to the decay of dissociated excited atoms. The purpose of this article is to review the very large variety of systems where such a situation has been encountered, extending from simple diatomic molecules toward more complex systems like polyatomics, clusters, or adsorbed molecules. Interestingly, this phenomenon has revealed an extremely rich and powerful tool for probing nuclear dynamics and its subtle interplay with electron relaxation occurring on a comparable time scale. Consequently this review covers a surprisingly large period, starting in 1986 and still ongoing.

The Shutdown Dissociation Scale (Shut-D)

Science.gov (United States)

Schalinski, Inga; Schauer, Maggie; Elbert, Thomas

2015-01-01

The evolutionary model of the defense cascade by Schauer and Elbert (2010) provides a theoretical frame for a short interview to assess problems underlying and leading to the dissociative subtype of posttraumatic stress disorder. Based on known characteristics of the defense stages “fright,” “flag,” and “faint,” we designed a structured interview to assess the vulnerability for the respective types of dissociation. Most of the scales that assess dissociative phenomena are designed as self-report questionnaires. Their items are usually selected based on more heuristic considerations rather than a theoretical model and thus include anything from minor dissociative experiences to major pathological dissociation. The shutdown dissociation scale (Shut-D) was applied in several studies in patients with a history of multiple traumatic events and different disorders that have been shown previously to be prone to symptoms of dissociation. The goal of the present investigation was to obtain psychometric characteristics of the Shut-D (including factor structure, internal consistency, retest reliability, predictive, convergent and criterion-related concurrent validity). A total population of 225 patients and 68 healthy controls were accessed. Shut-D appears to have sufficient internal reliability, excellent retest reliability, high convergent validity, and satisfactory predictive validity, while the summed score of the scale reliably separates patients with exposure to trauma (in different diagnostic groups) from healthy controls. The Shut-D is a brief structured interview for assessing the vulnerability to dissociate as a consequence of exposure to traumatic stressors. The scale demonstrates high-quality psychometric properties and may be useful for researchers and clinicians in assessing shutdown dissociation as well as in predicting the risk of dissociative responding. PMID:25976478

The Shutdown Dissociation Scale (Shut-D

Directory of Open Access Journals (Sweden)

Inga Schalinski

2015-05-01

Full Text Available The evolutionary model of the defense cascade by Schauer and Elbert (2010 provides a theoretical frame for a short interview to assess problems underlying and leading to the dissociative subtype of posttraumatic stress disorder. Based on known characteristics of the defense stages “fright,” “flag,” and “faint,” we designed a structured interview to assess the vulnerability for the respective types of dissociation. Most of the scales that assess dissociative phenomena are designed as self-report questionnaires. Their items are usually selected based on more heuristic considerations rather than a theoretical model and thus include anything from minor dissociative experiences to major pathological dissociation. The shutdown dissociation scale (Shut-D was applied in several studies in patients with a history of multiple traumatic events and different disorders that have been shown previously to be prone to symptoms of dissociation. The goal of the present investigation was to obtain psychometric characteristics of the Shut-D (including factor structure, internal consistency, retest reliability, predictive, convergent and criterion-related concurrent validity.A total population of 225 patients and 68 healthy controls were accessed. Shut-D appears to have sufficient internal reliability, excellent retest reliability, high convergent validity, and satisfactory predictive validity, while the summed score of the scale reliably separates patients with exposure to trauma (in different diagnostic groups from healthy controls.The Shut-D is a brief structured interview for assessing the vulnerability to dissociate as a consequence of exposure to traumatic stressors. The scale demonstrates high-quality psychometric properties and may be useful for researchers and clinicians in assessing shutdown dissociation as well as in predicting the risk of dissociative responding.

The Dissociative Subtype of Posttraumatic Stress Disorder (PTSD) Among Adolescents: Co-Occurring PTSD, Depersonalization/Derealization, and Other Dissociation Symptoms.

Science.gov (United States)

Choi, Kristen R; Seng, Julia S; Briggs, Ernestine C; Munro-Kramer, Michelle L; Graham-Bermann, Sandra A; Lee, Robert C; Ford, Julian D

2017-12-01

The purpose of this study was to examine the co-occurrence of posttraumatic stress disorder (PTSD) and dissociation in a clinical sample of trauma-exposed adolescents by evaluating evidence for the depersonalization/derealization dissociative subtype of PTSD as defined by the DSM-5 and then examining a broader set of dissociation symptoms. A sample of treatment-seeking, trauma-exposed adolescents 12 to 16 years old (N = 3,081) from the National Child Traumatic Stress Network Core Data Set was used to meet the study objectives. Two models of PTSD/dissociation co-occurrence were estimated using latent class analysis, one with 2 dissociation symptoms and the other with 10 dissociation symptoms. After model selection, groups within each model were compared on demographics, trauma characteristics, and psychopathology. Model A, the depersonalization/derealization model, had 5 classes: dissociative subtype/high PTSD; high PTSD; anxious arousal; dysphoric arousal; and a low symptom/reference class. Model B, the expanded dissociation model, identified an additional class characterized by dissociative amnesia and detached arousal. These 2 models provide new information about the specific ways PTSD and dissociation co-occur and illuminate some differences between adult and adolescent trauma symptom expression. A dissociative subtype of PTSD can be distinguished from PTSD alone in adolescents, but assessing a wider range of dissociative symptoms is needed to fully characterize adolescent traumatic stress responses. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

International Nuclear Information System (INIS)

Tsai, Tai Ming; Wang, Wei Hui

2009-01-01

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

Energy Technology Data Exchange (ETDEWEB)

Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

2009-01-15

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Dissociation Energies of Diatomic Molecules

International Nuclear Information System (INIS)

Qun-Chao, Fan; Wei-Guo, Sun

2008-01-01

Molecular dissociation energies of 10 electronic states of alkali molecules of KH, 7 LiD, 7 LiH, 6 LiH, NaK, NaLi and NaRb are studied using the highest three accurate vibrational energies of each electronic state, and an improved parameter-free analytical formula which is obtained starting from the LeRoy–Bernstein vibrational energy expression near the dissociation limit. The results show that as long as the highest three vibrational energies are accurate, the current analytical formula will give accurate theoretical dissociation energies D e theory , which are in excellent agreement with the experimental dissociation energies D e expt . (atomic and molecular physics)

The critical release rates for the dissociating gas N204/N02/N0

International Nuclear Information System (INIS)

Porter, W.H.L.

1979-03-01

Dissociating vapour systems have certain characteristics which make them attractive as coolants, notably a large effective specific heat which is significantly greater than that for the individual components of the gas mixture, and also an enhanced boundary layer heat transfer coefficient resulting from the physical characteristics of thermal dissociation. In part these effects ensure that a dissociating gas has a greatly improved thermal capacity and heat transfer capability when compared with most inert gases. In this report the critical release rates for the dissociating vapour system N 2 0 4 -N0 2 -N0 are established, principally in the two phase region, and the thermodynamics of nitrogen tetroxide are examined. (U.K.)

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

International Nuclear Information System (INIS)

Kolb, Brian; Zhao, Bin; Guo, Hua; Li, Jun; Jiang, Bin

2016-01-01

The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H 2 → H 2 + H, H + H 2 O → H 2 + OH, and H + CH 4 → H 2 + CH 3 . A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.

Neural network-based expert system for severe accident management

International Nuclear Information System (INIS)

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

1992-01-01

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

Neural Network Target Identification System for False Alarm Reduction

Science.gov (United States)

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

2009-01-01

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

Dissociative amnesia in dissociative disorders and borderline personality disorder: self-rating assessment in a college population.

Science.gov (United States)

Sar, Vedat; Alioğlu, Firdevs; Akyuz, Gamze; Karabulut, Sercan

2014-01-01

Dissociative amnesia (DA) among subjects with a dissociative disorder and/or borderline personality disorder (BPD) recruited from a nonclinical population was examined. The Steinberg Dissociative Amnesia Questionnaire (SDAQ), the Childhood Trauma Questionnaire, and the self-report screening tool of the BPD section of the Structured Clinical Interview for DSM-IV(SCID-BPD) were administered to 1,301 college students. A total of 80 participants who were diagnosed with BPD according to the clinician-administered SCID-BPD and 111 nonborderline controls were evaluated using the Structured Clinical Interview for DSM-IV Dissociative Disorders (SCID-D) by a psychiatrist blind to diagnosis and scale scores. Internal consistency analyses and test-retest evaluations suggested that the SDAQ is a reliable instrument for the population studied. Of the participants, 20.6% reported an SDAQ score of 20 or above and impairment by DA. Those who had both dissociative disorder and BPD (n = 78) had the highest SDAQ scores. Both disorders had significant effects on the SCID-D total and amnesia scores in the variance analysis. On SDAQ scores, however, only BPD had this effect. There was a significant interaction between the 2 disorders for the SCID-D total but not for the SDAQ or SCID-D amnesia scores. BPD represented the severity of dissociation and childhood trauma in this study group. However, in contrast to the dissociative disorders, BPD was characterized by better awareness of DA in self-report. The discrepancies between self-report and clinical interview associated with BPD and dissociative disorders are discussed in the context of betrayal theory (J. J. Freyd, 1994) of BPD and perceptual theory (D. B. Beere, 2009) of dissociative disorders.

Real-time camera-based face detection using a modified LAMSTAR neural network system

Science.gov (United States)

Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

2003-03-01

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

neural control system

International Nuclear Information System (INIS)

Elshazly, A.A.E.

2002-01-01

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

The LILARTI neural network system

Energy Technology Data Exchange (ETDEWEB)

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

1992-10-01

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

Gene expression profiles in relation to tension and dissociation in borderline personality disorder.

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Christian Schmahl

Full Text Available The biological underpinnings of borderline personality disorder (BPD and its psychopathology including states of aversive tension and dissociation is poorly understood. Our goal was to examine transcriptional changes associated with states of tension or dissociation within individual patients in a pilot study. Dissociation is not only a critical symptom of BPD but has also been associated with higher risk for self-mutilation and depression. We conducted a whole blood gene expression profile analysis using quantitative PCR in 31 female inpatients with BPD. For each individual, two samples were drawn during a state of high tension and dissociation, while two samples were drawn at non-tension states. There was no association between gene expression and tension states. However, we could show that Interleukin-6 was positively correlated to dissociation scores, whereas Guanine nucleotide-binding protein G(s subunit alpha isoforms, Mitogen-activated protein kinase 3 and 8, Guanine nucleotide-binding protein G(i subunit alpha-2, Beta-arrestin-1 and 2, and Cyclic AMP-responsive element-binding protein were negatively correlated to dissociation. Our data point to a potential association of dissociation levels with the expression of genes involved in immune system regulation as well as cellular signalling/second-messenger systems. Major limitations of the study are the the possibly heterogeneous cell proportions in whole blood and the heterogeneous medication.

[Clinical Handling of Patients with Dissociative Disorders].

Science.gov (United States)

Okano, Kenichiro

2015-01-01

This paper discusses the way informed psychiatrists are expected to handle dissociative patients in clinical situations, with a specific focus on dissociative identity disorders and dissociative fugue. On the initial interview with dissociative patients, information on their history of trauma and any nascent dissociative symptoms in their childhood should be carefully obtained. Their level of stress in their current life should also be assessed in order to understand their symptomatology, as well as to predict their future clinical course. A psychoeducational approach is crucial; it might be helpful to give information on dissociative disorder to these patients as well as their family members in order to promote their adherence to treatment. Regarding the symptomatology of dissociative disorders, detailed symptoms and the general clinical course are presented. It was stressed that dissociative identity disorder and dissociative fugue, the most high-profile dissociative disorders, are essentially different in their etiology and clinical presentation. Dissociative disorders are often confused with and misdiagnosed as psychotic disorders, such as schizophrenia. Other conditions considered in terms of the differential diagnosis include borderline personality disorder as well as temporal lobe epilepsy. Lastly, the therapeutic approach to dissociative identity disorder is discussed. Each dissociative identity should be understood as potentially representing some traumatically stressful event in the past. The therapist should be careful not to excessively promote the creation or elaboration of any dissociative identities. Three stages are proposed in the individual psychotherapeutic process. In the initial stage, a secure environment and stabilization of symptoms should be sought. The second stage consists of aiding the "host" personality to make use of other more adaptive coping skills in their life. The third stage involves coaching as well as continuous awareness of

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

Science.gov (United States)

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

2016-01-08

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

A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

OpenAIRE

Xiangyu He; Shanghong He

2014-01-01

Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

Neural System Prediction and Identification Challenge

Directory of Open Access Journals (Sweden)

Ioannis eVlachos

2013-12-01

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

Neural system prediction and identification challenge.

Science.gov (United States)

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

2013-01-01

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

Neural basis of self and other representation in autism: an FMRI study of self-face recognition.

Directory of Open Access Journals (Sweden)

Lucina Q Uddin

Full Text Available Autism is a developmental disorder characterized by decreased interest and engagement in social interactions and by enhanced self-focus. While previous theoretical approaches to understanding autism have emphasized social impairments and altered interpersonal interactions, there is a recent shift towards understanding the nature of the representation of the self in individuals with autism spectrum disorders (ASD. Still, the neural mechanisms subserving self-representations in ASD are relatively unexplored.We used event-related fMRI to investigate brain responsiveness to images of the subjects' own face and to faces of others. Children with ASD and typically developing (TD children viewed randomly presented digital morphs between their own face and a gender-matched other face, and made "self/other" judgments. Both groups of children activated a right premotor/prefrontal system when identifying images containing a greater percentage of the self face. However, while TD children showed activation of this system during both self- and other-processing, children with ASD only recruited this system while viewing images containing mostly their own face.This functional dissociation between the representation of self versus others points to a potential neural substrate for the characteristic self-focus and decreased social understanding exhibited by these individuals, and suggests that individuals with ASD lack the shared neural representations for self and others that TD children and adults possess and may use to understand others.

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Directory of Open Access Journals (Sweden)

Xiao-Li Li

2014-01-01

Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

Dissociation of ethane by electron impact

International Nuclear Information System (INIS)

Winters, H.F.

1979-01-01

The absolute total dissociation cross section for ethane is reported for electron energies between 10 and 600 eV. A maximum value of 7.6 X 10 -16 cm 2 occurs at 80 eV while the apparent threshold is approximately 10 eV. Dissociative ionization is more probable than dissociation into neutral fragments at all energies except in the threshold region. The data indicates that fragmentation involving methane elimination (e - +C 2 H 6 → CH 4 + CH 2 ) occurs in less than 2% of the dissociative events for 50 < E < 600 eV. Arguments are presented which suggest that some of the lower excited states of ethane are stable against dissociation. (Auth.)

Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

Energy Technology Data Exchange (ETDEWEB)

Gu, Z.; Rizy, D.T.

1996-02-01

A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

Reconsidering the autohypnotic model of the dissociative disorders.

Science.gov (United States)

Dell, Paul F

2018-03-22

The dissociative disorders field and the hypnosis field currently reject the autohypnotic model of the dissociative disorders, largely because many correlational studies have shown hypnotizability and dissociation to be minimally related (r = .12). Curiously, it is also widely accepted that dissociative patients are highly hypnotizable. If dissociative patients are highly hypnotizable because only highly hypnotizable individuals can develop a dissociative disorder - as the author proposes - then the methodology of correlational studies of hypnotizability and dissociation in random clinical and community samples would necessarily be constitutively unable to detect, and statistically unable to reflect, that fact. That is, the autohypnotic, dissociative distancing of that small subset of highly hypnotizable individuals who repeatedly encountered intolerable circumstances is statistically lost among the data of (1) the highly hypnotizable subjects who do not dissociate and (2) subjects (of all levels of hypnotizability) who manifest other kinds of dissociation. The author proposes that, when highly hypnotizable individuals repeatedly engage in autohypnotic distancing from intolerable circumstances, they develop an overlearned, highly-motivated, automatized pattern of dissociative self-protection (i.e., a dissociative disorder). The author urges that theorists of hypnosis and the dissociative disorders explicitly include in their theories (a) the trait of high hypnotizability, (b) the phenomena of autohypnosis, and (c) the manifestations of systematized, autohypnotic pathology. Said differently, the author is suggesting that autohypnosis and autohypnotic pathology are unacknowledged nodes in the nomothetic networks of both hypnosis and dissociation.

Testing the diagnosis of dissociative identity disorder through measures of dissociation, absorption, hypnotizability and PTSD: a Norwegian pilot study.

Science.gov (United States)

Dale, Karl Yngvar; Berg, Renate; Elden, Ake; Ødegård, Atle; Holte, Arne

2009-01-01

A total of 14 women meeting criteria for dissociative identity disorder (DID) based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM-IV]) were compared to a group of women (n = 10) with other dissociative diagnoses and a group of normal controls (n = 14) with regard to dissociativity, absorption, trauma related symptoms and hypnotizability. Both of the clinical groups reported histories of childhood trauma and attained high PTSD scores. The DID group differed significantly from the group with other dissociative diagnoses and the non-diagnosed comparison group with regard to hypnotizability, the variety of dissociative symptomatology, and the magnitude of dissociative symptomatology. However, no significant differences between the two clinical groups were detected with regard to absorption, general dissociative level, or symptoms related to traumatic stress. Results support the notion that DID can be regarded as a clinical entity which is separable from other dissociative disorders. Results also indicated that hypnotizability is the most important clinical feature of DID.

Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

International Nuclear Information System (INIS)

Wei Duqu; Luo Xiaoshu; Zou Yanli

2008-01-01

We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

Directory of Open Access Journals (Sweden)

David Breuer

2014-03-01

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

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

International Nuclear Information System (INIS)

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

2011-01-01

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

Dissociable Brain Signatures of Choice Conflict and Immediate Reward Preferences in Alcohol Use Disorders

Science.gov (United States)

Amlung, Michael; Sweet, Lawrence H.; Acker, John; Brown, Courtney L.; MacKillop, James

2013-01-01

Impulsive delayed reward discounting (DRD) is an important behavioral process in alcohol use disorders (AUDs), reflecting incapacity to delay gratification. Recent work in neuroeconomics has begun to unravel the neural mechanisms supporting DRD, but applications of neuroeconomics in relation to AUDs have been limited. This study examined the neural mechanisms of DRD preferences in AUDs, with emphasis on dissociating activation patterns based on DRD choice type and level of cognitive conflict. Heavy drinking adult males with (n = 13) and without (n = 12) a diagnosis of an AUD completed a monetary DRD task during a functional magnetic resonance imaging scan. Participant responses were coded based on choice type (impulsive vs. restrained) and level of cognitive conflict (easy vs. hard). AUD+ participants exhibited significantly more impulsive DRD decision-making. Significant activation during DRD was found in several decision-making regions, including dorsolateral prefrontal cortex (DLPFC), insula, posterior parietal cortex (PPC), and posterior cingulate. An axis of cognitive conflict was also observed, with hard choices associated with anterior cingulate cortex and easy choices associated with activation in supplementary motor area. AUD+ individuals exhibited significant hyperactivity in regions associated with cognitive control (DLPFC) and prospective thought (PPC) and exhibited less task-related deactivation of areas associated with the brain's default network during DRD decisions. This study provides further clarification of the brain systems supporting DRD in general and in relation to AUDs. PMID:23231650

Dissociation in undergraduate students: disruptions in executive functioning.

Science.gov (United States)

Giesbrecht, Timo; Merckelbach, Harald; Geraerts, Elke; Smeets, Ellen

2004-08-01

The concept of dissociation refers to disruptions in attentional control. Attentional control is an executive function. Few studies have addressed the link between dissociation and executive functioning. Our study investigated this relationship in a sample of undergraduate students (N = 185) who completed the Dissociative Experiences Scale and the Random Number Generation Task. We found that minor disruptions in executive functioning were related to a subclass of dissociative experiences, notably dissociative amnesia and the Dissociative Experiences Scale Taxon. However, the two other subscales of the Dissociative Experiences Scale, measuring depersonalization and absorption, were unrelated to executive functioning. Our findings suggest that a failure to inhibit previous responses might contribute to the pathological memory manifestations of dissociation.

Barrierless association of CF2 and dissociation of C2F4 by variational transition-state theory and system-specific quantum Rice–Ramsperger–Kassel theory

Science.gov (United States)

Bao, Junwei Lucas; Zhang, Xin

2016-01-01

Bond dissociation is a fundamental chemical reaction, and the first principles modeling of the kinetics of dissociation reactions with a monotonically increasing potential energy along the dissociation coordinate presents a challenge not only for modern electronic structure methods but also for kinetics theory. In this work, we use multifaceted variable-reaction-coordinate variational transition-state theory (VRC-VTST) to compute the high-pressure limit dissociation rate constant of tetrafluoroethylene (C2F4), in which the potential energies are computed by direct dynamics with the M08-HX exchange correlation functional. To treat the pressure dependence of the unimolecular rate constants, we use the recently developed system-specific quantum Rice–Ramsperger–Kassel theory. The calculations are carried out by direct dynamics using an exchange correlation functional validated against calculations that go beyond coupled-cluster theory with single, double, and triple excitations. Our computed dissociation rate constants agree well with the recent experimental measurements. PMID:27834727

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

Science.gov (United States)

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

Adaptive neural networks control for camera stabilization with active suspension system

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-08-01

Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

Neural systems for preparatory control of imitation.

Science.gov (United States)

Cross, Katy A; Iacoboni, Marco

2014-01-01

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

Dissociated cultures of newborn mouse brain

International Nuclear Information System (INIS)

Wiesmann, U.N.; Hofmann, K.; Burkhart, T.; Herschkowitz, N.

1975-01-01

The metabolism of 35 SO 4 -sulfated lipids and mucopolysaccharides was studied in dissociated brain cell cultures from newborn albino mouse brains. The cultures were maintained under an atmosphere of 40% O 2 and 5% CO 2 in apparent good health up to 30 days. Early morphological examination of the dissociated cells demonstrated an initial partial reaggregation of the cells, which later settled and became confluent bilayered cultures. Cell proliferation measured by DNA and protein determination, morphological differentiation and biochemical differentiation took place in the dissociated brain cell cultures analogous in some respects to the in vivo situation. A timed increase in the synthesis of a myelin precursor, cerebroside 35 SO 4 , was observed after 6 to 8 days in culture (DIC). A peak of cerebroside sulfate was evident at 17 DIC. No stable sulfatide was observed at any time. Protein-bound macromolecular 35 SO 4 -MPS was synthetized and secreted from the cells into the culture medium. Maximal synthesis and secretion occurred at 8 DIC. This culture system proves to be a useful model for studying some aspects of differentiation of brain cells under external conditions. (author)

On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

Science.gov (United States)

Song, Tao; Xu, Jinbang; Pan, Linqiang

2015-12-01

Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

A neural network approach to the study of dynamics and structure of molecular systems

International Nuclear Information System (INIS)

Getino, C.; Sumpter, B.G.; Noid, D.W.

1994-01-01

Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

Prototype learning and dissociable categorization systems in Alzheimer's disease.

Science.gov (United States)

Heindel, William C; Festa, Elena K; Ott, Brian R; Landy, Kelly M; Salmon, David P

2013-08-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer's disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Directory of Open Access Journals (Sweden)

Biaobiao Zhang

2011-01-01

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

Comparing the symptoms and mechanisms of "dissociation" in dissociative identity disorder and borderline personality disorder.

Science.gov (United States)

Laddis, Andreas; Dell, Paul F; Korzekwa, Marilyn

2017-01-01

A total of 75 patients were diagnosed with the Structured Clinical Interview for DSM-IV Dissociative Disorders-Revised as having dissociative identity disorder (DID), and 100 patients were diagnosed with the Structured Interview for DSM-IV Personality as having borderline personality disorder (BPD). Both groups were administered the Multidimensional Inventory of Dissociation (MID). DID patients had significantly higher MID scores than BPD patients, different distributions of MID scores, and different MID subscale profiles in 3 ranges of MID scores (0-15, 15-30, 30-45). The core MID symptoms-exhibited at all ranges of MID scores-for DID patients (the presence of alters, identity confusion, and memory problems) and BPD patients (flashbacks, identity confusion, and memory problems) were ostensibly similar but were considered to be mostly produced by different underlying processes. Multiple regression analyses showed that the core MID symptoms of DID patients had different predictors than did the core MID symptoms of BPD patients. Alter identities seemed to generate most-but not all-dissociative phenomena in DID patients, whereas only the 24% highest scoring BPD patients (MID ≥45) seemed to manifest alter-driven dissociative experiences. Most BPD dissociative experiences appeared to be due to 5 other mechanisms: (a) BPD-specific, stress-driven, rapid shifts of self-state; (b and c) nondefensive disruptions of the framework of perceptual organization with or without an accompanying BPD-specific, dissociation-like disintegration of affective/neurocognitive functioning; (d) a defensive distancing or detachment from distress (i.e., simple depersonalization); and (e) Allen, Console, and Lewis's (1999) severe absorptive detachment.

Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

Directory of Open Access Journals (Sweden)

Kun Zhang

2015-01-01

Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

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Xu, Bin; Yang, Chenguang; Pan, Yongping

2015-10-01

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

Rovibrational internal energy transfer and dissociation of N2(1Σg+)-N(4S(u)) system in hypersonic flows.

Science.gov (United States)

Panesi, Marco; Jaffe, Richard L; Schwenke, David W; Magin, Thierry E

2013-01-28

A rovibrational collisional model is developed to study energy transfer and dissociation of N(2)((1)Σ(g)(+)) molecules interacting with N((4)S(u)) atoms in an ideal isochoric and isothermal chemical reactor. The system examined is a mixture of molecular nitrogen and a small amount of atomic nitrogen. This mixture, initially at room temperature, is heated by several thousands of degrees Kelvin, driving the system toward a strong non-equilibrium condition. The evolution of the population densities of each individual rovibrational level is explicitly determined via the numerical solution of the master equation for temperatures ranging from 5000 to 50,000 K. The reaction rate coefficients are taken from an ab initio database developed at NASA Ames Research Center. The macroscopic relaxation times, energy transfer rates, and dissociation rate coefficients are extracted from the solution of the master equation. The computed rotational-translational (RT) and vibrational-translational (VT) relaxation times are different at low heat bath temperatures (e.g., RT is about two orders of magnitude faster than VT at T = 5000 K), but they converge to a common limiting value at high temperature. This is contrary to the conventional interpretation of thermal relaxation in which translational and rotational relaxation timescales are assumed comparable with vibrational relaxation being considerable slower. Thus, this assumption is questionable under high temperature non-equilibrium conditions. The exchange reaction plays a very significant role in determining the dynamics of the population densities. The macroscopic energy transfer and dissociation rates are found to be slower when exchange processes are neglected. A macroscopic dissociation rate coefficient based on the quasi-stationary distribution, exhibits excellent agreement with experimental data of Appleton et al. [J. Chem. Phys. 48, 599-608 (1968)]. However, at higher temperatures, only about 50% of dissociation is found to

Biological neural networks as model systems for designing future parallel processing computers

Science.gov (United States)

Ross, Muriel D.

1991-01-01

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

Functional dissociation of the left and right fusiform gyrus in self-face recognition.

Science.gov (United States)

Ma, Yina; Han, Shihui

2012-10-01

It is well known that the fusiform gyrus is engaged in face perception, such as the processes of face familiarity and identity. However, the functional role of the fusiform gyrus in face processing related to high-level social cognition remains unclear. The current study assessed the functional role of individually defined fusiform face area (FFA) in the processing of self-face physical properties and self-face identity. We used functional magnetic resonance imaging to monitor neural responses to rapidly presented face stimuli drawn from morph continua between self-face (Morph 100%) and a gender-matched friend's face (Morph 0%) in a face recognition task. Contrasting Morph 100% versus Morph 60% that differed in self-face physical properties but were both recognized as the self uncovered neural activity sensitive to self-face physical properties in the left FFA. Contrasting Morphs 50% that were recognized as the self versus a friend on different trials revealed neural modulations associated with self-face identity in the right FFA. Moreover, the right FFA activity correlated with the frequency of recognizing Morphs 50% as the self. Our results provide evidence for functional dissociations of the left and right FFAs in the representations of self-face physical properties and self-face identity. Copyright © 2011 Wiley Periodicals, Inc.

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

International Nuclear Information System (INIS)

Dominguez, Manuel

1998-01-01

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

Dissociative Part-Dependent Resting-State Activity in Dissociative Identity Disorder: A Controlled fMRI Perfusion Study

OpenAIRE

Schlumpf, Yolanda R.; Reinders, Antje A. T. S.; Nijenhuis, Ellert R. S.; Luechinger, Roger; van Osch, Matthias J. P.; Jäncke, Lutz

2014-01-01

Background: In accordance with the Theory of Structural Dissociation of the Personality (TSDP), studies of dissociative identity disorder (DID) have documented that two prototypical dissociative subsystems of the personality, the "Emotional Part'' (EP) and the "Apparently Normal Part'' (ANP), have different biopsychosocial reactions to supraliminal and subliminal trauma-related cues and that these reactions cannot be mimicked by fantasy prone healthy controls nor by actors. Methods: Arterial ...

Dissociable contributions of the amygdala to the immediate and delayed effects of emotional arousal on memory.

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Schümann, Dirk; Sommer, Tobias

2018-06-01

Emotional arousal enhances memory encoding and consolidation leading to better immediate and delayed memory. Although the central noradrenergic system and the amygdala play critical roles in both effects of emotional arousal, we have recently shown that these effects are at least partly independent of each other, suggesting distinct underlying neural mechanisms. Here we aim to dissociate the neural substrates of both effects in 70 female participants using an emotional memory paradigm to investigate how neural activity, as measured by fMRI, and a polymorphism in the α 2B -noradrenoceptor vary for these effects. To also test whether the immediate and delayed effects of emotional arousal on memory are stable traits, we invited back participants who were a part of a large-scale behavioral memory study ∼3.5 yr ago. We replicated the low correlation of the immediate and delayed emotional enhancement of memory across participants ( r = 0.16) and observed, moreover, that only the delayed effect was, to some degree, stable over time ( r = 0.23). Bilateral amygdala activity, as well as its coupling with the visual cortex and the fusiform gyrus, was related to the preferential encoding of emotional stimuli, which is consistent with affect-biased attention. Moreover, the adrenoceptor genotype modulated the bilateral amygdala activity associated with this effect. The left amygdala and its coupling with the hippocampus was specifically associated with the more efficient consolidation of emotional stimuli, which is consistent with amygdalar modulation of hippocampal consolidation. © 2018 Schümann and Sommer; Published by Cold Spring Harbor Laboratory Press.

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

International Nuclear Information System (INIS)

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

1991-01-01

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

The ctenophore genome and the evolutionary origins of neural systems

NARCIS (Netherlands)

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

2014-01-01

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

Brain functional integration: an epidemiologic study on stress-producing dissociative phenomena

Science.gov (United States)

Messina, Giovanni; Carotenuto, Marco; Maldonato, Nelson Mauro; Moretto, Enrico; Leone, Elena; De Luca, Vincenzo; Monda, Marcellino; Messina, Antonietta

2018-01-01

Dissociative phenomena are common among psychiatric patients; the presence of these symptoms can worsen the prognosis, increasing the severity of their clinical conditions and exposing them to increased risk of suicidal behavior. Personality disorders as long duration stressful experiences may support the development of dissociative phenomena. In 933 psychiatric outpatients consecutively recruited, presence of dissociative phenomena was identified with the Dissociative Experience Scale (DES). Dissociative phenomena were significantly more severe in the group of people with mental disorders and/or personality disorders. All psychopathologic traits detected with the symptom checklist-90-revised had a significant correlation with the total score on the DES. Using total DES score as the dependent variable, a linear regression model was constructed. Mental and personality disorders which were associated with greater severity of dissociative phenomena on analysis of variance were included as predictors; scores from the nine scales of symptom checklist-90-revised, significantly correlated to total DES score, were used as covariates. The model consisted of seven explanatory variables (four factors and three covariates) explaining 82% of variance. The four significant factors were the presence of borderline and narcissistic personality disorder, substance abuse disorders and psychotic disorders. Significant covariates were psychopathologic traits of anger, psychoticism and obsessiveness. This study, confirming Janet’s theory, explains that, mental disorders and psychopathologic experiences of patients can configure the chronic stress condition that produces functional damage to the adaptive executive system. The symptoms of dissociative depersonalization/derealization and dissociative amnesia can be explained, in large part, through their current and previous psychopathologic experiences. PMID:29296086

[Questionnaire on dissociative symptoms. German adaptation, reliability and validity of the American Dissociative Experience Scale (DES)].

Science.gov (United States)

Freyberger, H J; Spitzer, C; Stieglitz, R D; Kuhn, G; Magdeburg, N; Bernstein-Carlson, E

1998-06-01

The "Fragebogen zu dissoziativen Symptomen (FDS)" represents the authorised German translation and adaptation of the "Dissociative Experience Scale" (DES; Bernstein and Putnam 1986). The original scale comprises 28 items covering dissociative experiences with regard to memory, identity, awareness and cognition according to DSM-III-R and DSM-IV. For the German version, 16 items were added to cover dissociative phenomena according to ICD-10, mainly pseudoneurological conversion symptoms. Reliability and validity of the German version were studied in a total sample of 813 persons and were compared to the results of the original version. Test-retest reliability of the FDS was rtt = 0.88 and Cronbach's consistency coefficient was alpha = 0.93, which is comparable to the results of the DES. The instrument differentiates between different samples (healthy control subjects, students, unselected neurological and psychiatric inpatients, neurological and psychiatric patients with a dissociative disorder and schizophrenics). The FDS is an easily applicable, reliable and valid measure to quantify dissociative experiences.

The Neural Correlates of Implicit and Explicit Sequence Learning: Interacting Networks Revealed by the Process Dissociation Procedure

Science.gov (United States)

Laureys, Steven; Degueldre, Christian; Del Fiore, Guy; Aerts, Joel; Luxen, Andre; Van Der Linden, Martial; Cleeremans, Axel; Maquet, Pierre; Destrebecqz, Arnaud; Peigneux, Philippe

2005-01-01

In two H[subscript 2] [superscript 15]O PET scan experiments, we investigated the cerebral correlates of explicit and implicit knowledge in a serial reaction time (SRT) task. To do so, we used a novel application of the Process Dissociation Procedure, a behavioral paradigm that makes it possible to separately assess conscious and unconscious…

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

Energy Technology Data Exchange (ETDEWEB)

Kolb, Brian [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Zhao, Bin; Guo, Hua, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Li, Jun [School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331 (China); Jiang, Bin [Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China)

2016-06-14

The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H{sub 2} → H{sub 2} + H, H + H{sub 2}O → H{sub 2} + OH, and H + CH{sub 4} → H{sub 2} + CH{sub 3}. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.

Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia.

Directory of Open Access Journals (Sweden)

Rainer Stollhoff

Full Text Available The study investigates long-term recognition memory in congenital prosopagnosia (CP, a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs. In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception.

Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia.

Science.gov (United States)

Stollhoff, Rainer; Jost, Jürgen; Elze, Tobias; Kennerknecht, Ingo

2011-01-25

The study investigates long-term recognition memory in congenital prosopagnosia (CP), a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year) recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs). In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception.

Dissociative Disorders Among Chinese Inpatients Diagnosed With Schizophrenia

Science.gov (United States)

Yu, Junhan; Ross, Colin A.; Keyes, Benjamin B.; Li, Ying; Dai, Yunfei; Zhang, Tianhong; Wang, Lanlan; Fan, Qing; Xiao, Zeping

2010-01-01

The purpose of the study was to assess the prevalence of dissociative disorders in a sample of Chinese psychiatric inpatients. Participants in the study consisted of 569 consecutively admitted inpatients at Shanghai Mental Health Center, China, of whom 84.9% had a clinical diagnosis of schizophrenia based on the Chinese Classification and Diagnostic Criteria for Mental Disorders, Version 3 (CCMD-3). All participants completed a self-report measure of dissociation, the Dissociative Experiences Scale (DES) and none had a prior diagnosis of a dissociative disorder. Ninety-six randomly selected participants were interviewed with a structured interview, the Dissociative Disorders Interview Schedule (DDIS) and a clinical interview. These 96 patients did not differ significantly from the 473 patients who were not interviewed on any demographic measures or on the self-report measure dissociation. A total of 28 (15.3%, after weighting of the data) patients received a clinical diagnosis of a dissociative disorder based on DSM-IV-TR criteria. Dissociative identity disorder was diagnosed in 2 (0.53%, after weighting) patients. Compared to the patients without a dissociative disorder, patients with dissociative disorders were significantly more likely to report childhood abuse (57.1% versus 22.1%), but the two groups did not differ significantly on any demographic measures. Dissociative disorders were readily identified in an inpatient psychiatric population in China. PMID:20603768

A Double Dissociation between Anterior and Posterior Superior Temporal Gyrus for Processing Audiovisual Speech Demonstrated by Electrocorticography.

Science.gov (United States)

Ozker, Muge; Schepers, Inga M; Magnotti, John F; Yoshor, Daniel; Beauchamp, Michael S

2017-06-01

Human speech can be comprehended using only auditory information from the talker's voice. However, comprehension is improved if the talker's face is visible, especially if the auditory information is degraded as occurs in noisy environments or with hearing loss. We explored the neural substrates of audiovisual speech perception using electrocorticography, direct recording of neural activity using electrodes implanted on the cortical surface. We observed a double dissociation in the responses to audiovisual speech with clear and noisy auditory component within the superior temporal gyrus (STG), a region long known to be important for speech perception. Anterior STG showed greater neural activity to audiovisual speech with clear auditory component, whereas posterior STG showed similar or greater neural activity to audiovisual speech in which the speech was replaced with speech-like noise. A distinct border between the two response patterns was observed, demarcated by a landmark corresponding to the posterior margin of Heschl's gyrus. To further investigate the computational roles of both regions, we considered Bayesian models of multisensory integration, which predict that combining the independent sources of information available from different modalities should reduce variability in the neural responses. We tested this prediction by measuring the variability of the neural responses to single audiovisual words. Posterior STG showed smaller variability than anterior STG during presentation of audiovisual speech with noisy auditory component. Taken together, these results suggest that posterior STG but not anterior STG is important for multisensory integration of noisy auditory and visual speech.

Functional Organization of the Parahippocampal Cortex: Dissociable Roles for Context Representations and the Perception of Visual Scenes.

Science.gov (United States)

Baumann, Oliver; Mattingley, Jason B

2016-02-24

The human parahippocampal cortex has been ascribed central roles in both visuospatial and mnemonic processes. More specifically, evidence suggests that the parahippocampal cortex subserves both the perceptual analysis of scene layouts as well as the retrieval of associative contextual memories. It remains unclear, however, whether these two functional roles can be dissociated within the parahippocampal cortex anatomically. Here, we provide evidence for a dissociation between neural activation patterns associated with visuospatial analysis of scenes and contextual mnemonic processing along the parahippocampal longitudinal axis. We used fMRI to measure parahippocampal responses while participants engaged in a task that required them to judge the contextual relatedness of scene and object pairs, which were presented either as words or pictures. Results from combined factorial and conjunction analyses indicated that the posterior section of parahippocampal cortex is driven predominantly by judgments associated with pictorial scene analysis, whereas its anterior section is more active during contextual judgments regardless of stimulus category (scenes vs objects) or modality (word vs picture). Activation maxima associated with visuospatial and mnemonic processes were spatially segregated, providing support for the existence of functionally distinct subregions along the parahippocampal longitudinal axis and suggesting that, in humans, the parahippocampal cortex serves as a functional interface between perception and memory systems. Copyright © 2016 the authors 0270-6474/16/362536-07$15.00/0.

System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

Science.gov (United States)

Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

2017-04-01

This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

Dissociative Tendencies and Traffic Incidents

Directory of Open Access Journals (Sweden)

Valle, Virginia

2012-01-01

Full Text Available This paper analyses the relationship between dissociative experiences and road traffic incidents (crashes and traffic tickets in drivers (n=295 from Mar del Plata (Argentina city. A self-report questionnaire was applied to assess traffic crash involvement and sociodemographic variables. Dissociative tendencies were assessed by a modified version of the DES scale. To examine differences in DES scores tests of the difference of means were applied. Drivers who reported to be previously involved in traffic incidents obtained higher puntuations in the dissociative experiences scale than drivers who did not report such events. This result is observed for the total scale and for the three sub-scales (absorption, amnesia and depersonalization. However, differences appeared mainly for minor damage collisions. Further studies are needed to evaluate the role of dissociative tendencies as a risk factor in road traffic safety.

Psychotherapy and pharmacotherapy for patients with dissociative identity disorder.

Science.gov (United States)

Gentile, Julie P; Dillon, Kristy S; Gillig, Paulette Marie

2013-02-01

There is a wide variety of what have been called "dissociative disorders," including dissociative amnesia, dissociative fugue, depersonalization disorder, dissociative identity disorder, and forms of dissociative disorder not otherwise specified. Some of these diagnoses, particularly dissociative identity disorder, are controversial and have been questioned by many clinicians over the years. The disorders may be under-diagnosed or misdiagnosed, but many persons who have experienced trauma report "dissociative" symptoms. Prevalence of dissociative disorders is unknown, but current estimates are higher than previously thought. This paper reviews clinical, phenomenological, and epidemiological data regarding diagnosis in general, and illustrates possible treatment interventions for dissociative identity disorder, with a focus on psychotherapy interventions and a review of current psychopharmacology recommendations as part of a comprehensive multidisciplinary treatment plan.

Differences between otolith- and semicircular canal-activated neural circuitry in the vestibular system.

Science.gov (United States)

Uchino, Yoshio; Kushiro, Keisuke

2011-12-01

In the last two decades, we have focused on establishing a reliable technique for focal stimulation of vestibular receptors to evaluate neural connectivity. Here, we summarize the vestibular-related neuronal circuits for the vestibulo-ocular reflex, vestibulocollic reflex, and vestibulospinal reflex arcs. The focal stimulating technique also uncovered some hidden neural mechanisms. In the otolith system, we identified two hidden neural mechanisms that enhance otolith receptor sensitivity. The first is commissural inhibition, which boosts sensitivity by incorporating inputs from bilateral otolith receptors, the existence of which was in contradiction to the classical understanding of the otolith system but was observed in the utricular system. The second mechanism, cross-striolar inhibition, intensifies the sensitivity of inputs from both sides of receptive cells across the striola in a single otolith sensor. This was an entirely novel finding and is typically observed in the saccular system. We discuss the possible functional meaning of commissural and cross-striolar inhibition. Finally, our focal stimulating technique was applied to elucidate the different constructions of axonal projections from each vestibular receptor to the spinal cord. We also discuss the possible function of the unique neural connectivity observed in each vestibular receptor system. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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

Science.gov (United States)

Gao, Rong

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

Neurophysiological correlates of dissociative symptoms

NARCIS (Netherlands)

Kruijs, van der S.J.M.; Bodde, N.M.G; Carrette, E.C.B.; Lazeron, R.H.C; Vonck, K.E.J.; Boon, P.A.J.M.; Langereis, G.R.; Cluitmans, P.J.M.; Feijs, L.M.G.; Hofman, P.A.M.; Backes, W.H.; Jansen, J.F.A.; Aldenkamp, A.P.

2014-01-01

Objective: Dissociation is a mental process with psychological and somatoform manifestations, which is closely related to hypnotic suggestibility and essentially shows the ability to obtain distance from reality. An increased tendency to dissociate is a frequently reported characteristic of patients

Dissociation: a developmental psychoneurobiological perspective

African Journals Online (AJOL)

Adele

... the stream of con- sciousness and memory.1 It is a frequent symptom of a range ... infant for long time spans in an extremely disturbed psycho- biological state that ... Advantage: Dissociation is adaptive in the short-term. Dissociation can be ...

Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty.

Science.gov (United States)

Luttrell, Andrew; Stillman, Paul E; Hasinski, Adam E; Cunningham, William A

2016-04-01

People's behaviors are often guided by valenced responses to objects in the environment. Beyond positive and negative evaluations, attitudes research has documented the importance of attitude strength--qualities of an attitude that enhance or attenuate its impact and durability. Although neuroscience research has extensively investigated valence, little work exists on other related variables like metacognitive judgments about one's attitudes. It remains unclear, then, whether the various indicators of attitude strength represent a single underlying neural process or whether they reflect independent processes. To examine this, we used functional MRI (fMRI) to identify the neural correlates of attitude strength. Specifically, we focus on ambivalence and certainty, which represent metacognitive judgments that people can make about their evaluations. Although often correlated, prior neuroscience research suggests that these 2 attributes may have distinct neural underpinnings. We investigate this by having participants make evaluative judgments of visually presented words while undergoing fMRI. After scanning, participants rated the degree of ambivalence and certainty they felt regarding their attitudes toward each word. We found that these 2 judgments corresponded to distinct brain regions' activity during the process of evaluation. Ambivalence corresponded to activation in anterior cingulate cortex, dorsomedial prefrontal cortex, and posterior cingulate cortex. Certainty, however, corresponded to activation in unique areas of the precuneus/posterior cingulate cortex. These results support a model treating ambivalence and certainty as distinct, though related, attitude strength variables, and we discuss implications for both attitudes and neuroscience research. (c) 2016 APA, all rights reserved).

Collision-induced dissociation of diatomic ions

International Nuclear Information System (INIS)

Los, J.; Govers, T.R.

1978-01-01

An attempt is made to illustrate how mass spectrometric studies of dissociation in diatomic molecular ions can provide information on the dynamics of these collisions and on the predissociative states involved. Restriction is made to primary beam energies of the order of at least keV. The review covers the dynamics of dissociation, experimental techniques, direct dissociation in heavy-particle collisions, and translational spectroscopy. 120 references

Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

Science.gov (United States)

Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

2018-03-01

Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

From state dissociation to status dissociatus.

Science.gov (United States)

Antelmi, Elena; Ferri, Raffaele; Iranzo, Alex; Arnulf, Isabelle; Dauvilliers, Yves; Bhatia, Kailash P; Liguori, Rocco; Schenck, Carlos H; Plazzi, Giuseppe

2016-08-01

The states of being are conventionally defined by the simultaneous occurrence of behavioral, neurophysiological and autonomic descriptors. State dissociation disorders are due to the intrusion of features typical of a different state into an ongoing state. Disorders related to these conditions are classified according to the ongoing main state and comprise: 1) Dissociation from prevailing wakefulness as seen in hypnagogic or hypnopompic hallucinations, automatic behaviors, sleep drunkenness, cataplexy and sleep paralysis 2) Dissociation from rapid eye movement (REM) sleep as seen in REM sleep behavior disorder and lucid dreaming and 3) Dissociation from NREM sleep as seen in the disorders of arousal. The extreme expression of states dissociation is characterized by the asynchronous occurrence of the various components of the different states that prevents the recognition of any state of being. This condition has been named status dissociatus. According to the underlying disorders/diseases and to their severity, among status dissociatus we may recognize disorders in which such an extreme dissociation occurs only at night time or intermittently (i.e., autoimmune encephalopathies, narcolepsy type 1 and IgLON5 parasomnia), and others in which it occurs nearly continuously with complete loss of any conventionally defined state of being, and of the circadian pattern (agrypnia excitata). Here, we render a comprehensive review of all diseases/disorders associated with state dissociation and status dissociatus and propose a critical classification of this complex scenario. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dissociation: Defining the Concept in Criminal Forensic Psychiatry.

Science.gov (United States)

Bourget, Dominique; Gagné, Pierre; Wood, Stephen Floyd

2017-06-01

Claims of amnesia and dissociative experiences in association with a violent crime are not uncommon. Research has shown that dissociation is a risk factor for violence and is seen most often in crimes of extreme violence. The subject matter is most relevant to forensic psychiatry. Peritraumatic dissociation for instance, with or without a history of dissociative disorder, is quite frequently reported by offenders presenting for a forensic psychiatric examination. Dissociation or dissociative amnesia for serious offenses can have legal repercussions stemming from their relevance to the legal constructs of fitness to stand trial, criminal responsibility, and diminished capacity. The complexity in forensic psychiatric assessments often lies in the difficulty of connecting clinical symptomatology reported by violent offenders to a specific condition included in the Diagnostic and Statistical Manual of Mental Disorders (DSM). This article provides a review of diagnostic considerations with regard to dissociation across the DSM nomenclature, with a focus on the main clinical constructs related to dissociation. Forensic implications are discussed, along with some guides for the forensic evaluator of offenders presenting with dissociation. © 2017 American Academy of Psychiatry and the Law.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Science.gov (United States)

Peng, Jinzhu; Dubay, Rickey

2011-10-01

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Psychotherapy and Pharmacotherapy for Patients with Dissociative Identity Disorder

OpenAIRE

Gentile, Julie P.; Dillon, Kristy S.; Gillig, Paulette Marie

2013-01-01

There is a wide variety of what have been called “dissociative disorders,” including dissociative amnesia, dissociative fugue, depersonalization disorder, dissociative identity disorder, and forms of dissociative disorder not otherwise specified. Some of these diagnoses, particularly dissociative identity disorder, are controversial and have been questioned by many clinicians over the years. The disorders may be under-diagnosed or misdiagnosed, but many persons who have experienced trauma rep...

Visuomotor Dissociation in Cerebral Scaling of Size

NARCIS (Netherlands)

Potgieser, Adriaan R. E.; de Jong, Bauke M.

2016-01-01

Estimating size and distance is crucial in effective visuomotor control. The concept of an internal coordinate system implies that visual and motor size parameters are scaled onto a common template. To dissociate perceptual and motor components in such scaling, we performed an fMRI experiment in

Energy distribution in dissociations of polyatomic molecules

International Nuclear Information System (INIS)

Koernig, S.A.

1989-01-01

In this thesis studies are reported of fragmentation processes in polyatomic molecules. In order to find out which dessocaciation reactions take place, how they are brought about by the internal energy of the reactant, and to investigate the structure of the dissociating 'transition state', the fragment mass and the corresponding kinetic energy release (KER) are determined by differential translational spectroscopy using a position and time sensitive two-particle coincidence detector. The results are interpreted using the statistical theory of unimolecular dissociation. It turns out that the standard assumptions of the theory, especially in calculating KER-distributions, are not realistic in all molecules considered. Dissociation is induced by the neutralization with alkali metal vapour. In ch. 2 the experimental method and the analysis of the data (dissociation pathways, branching ratios and ε-d-distributions) are introduced and exemplified by measurements of cyclohexane, which represents the upper limit in precursor and fragment mass accessible in the apparatus. In ch. 3 a study is reported of the molecules methylchloride (CH 3 Cl) and the acetylradical (CH 3 CO). In spite of their similar geometric structures, completely different dissociation mechanisms have been found. Methylchloride dissociates via a repulsive state; acetyl radicals show energy scrambling. The energy distribution from dissociating acetyl exemplifies dynamical effects in the dissociation. In ch. 4 an investigation of a number of prototype hydrocarbons is presented. The dissociation pathways of several small linear alkanes indicate that neutralization takes place to unknown repulsive potentials, of which the position and steepness are determined from the kinetic energy release. (author). 118 refs.; 40 figs.; 5 tabs

Neural Network Based Intrusion Detection System for Critical Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Ondrej Linda; Milos Manic

2009-07-01

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

Dissociative depression among women in the community.

Science.gov (United States)

Sar, Vedat; Akyüz, Gamze; Oztürk, Erdinç; Alioğlu, Firdevs

2013-01-01

This study screened the prevalence and correlates of dissociative disorders among depressive women in the general population. The Dissociative Disorders Interview Schedule and the posttraumatic stress disorder (PTSD) and borderline personality disorder sections of the Structured Clinical Interview for DSM-IV were administered to 628 women in 500 homes. The prevalence of current major depressive episode was 10.0%. Of the women, 26 (40.6%) had the lifetime diagnosis of a DSM-IV, dissociative disorder, yielding a prevalence of 4.1% for dissociative depression. This group was younger (mean age = 30.7 years) than the nondissociative depression women (mean age = 39.6 years). There was no difference between the 2 groups on comorbid somatization disorder, PTSD, or borderline personality disorder. Besides suicide attempts, the dissociative group was characterized by secondary features of dissociative identity disorder; Schneiderian symptoms; borderline personality disorder criteria; and extrasensory perceptions, including possession experiences. They reported suicidality, thoughts of guilt and worthlessness, diminished concentration and indecisiveness, and appetite and weight changes more frequently than the nondissociative group. Early cessation of school education and childhood sexual abuse were frequently reported by the dissociative depression group. With its distinct features, the concept of dissociative depression may facilitate understanding of treatment resistance in, development of better psychotherapy strategies for, and new thinking on the neurobiology and pharmacotherapy of depressive disorders.

Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

Directory of Open Access Journals (Sweden)

David Overbye

2005-06-01

Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

Words, Hemispheres, and Dissociable Subsystems: The Effects of Exposure Duration, Case Alternation, Priming, and Continuity of Form on Word Recognition in the Left and Right Visual Fields

Science.gov (United States)

Ellis, Andrew W.; Ansorge, Lydia; Lavidor, Michal

2007-01-01

Three experiments explore aspects of the dissociable neural subsystems theory of hemispheric specialisation proposed by Marsolek and colleagues, and in particular a study by [Deason, R. G., & Marsolek, C. J. (2005). A critical boundary to the left-hemisphere advantage in word processing. "Brain and Language," 92, 251-261]. Experiment 1A showed…

Duality in diffraction dissociations

International Nuclear Information System (INIS)

Santoro, Alberto.

1977-01-01

Diffractive dissociations (aN→a*πN) are naturally explained and a model that accounts for the three-variable correlation (mass-transfer-Jackson angle correlation) is presented. This model takes into account the three possible exchanges: t (pion), u(a*) and s(a) channel exchanger. The physical consequences of the model are: a strong mass-slope correlation due to the zeros of the amplitude, a factorization of diffractive dissociations (factorization of the Pomeron), the possibility of extending this model to double diffractive dissociation and diffraction by nuclei. This model was applied to the NN→NπN reaction. Using the usual parameters of the Deck model, a comparison is made with experiments for all available distributions. the strong slope of the peak at 1400 MeV is naturally explained [fr

Simple quantal model for collision-induced dissociation: An Airy basis calculation

International Nuclear Information System (INIS)

Hunt, P.M.; Sridharan, S.

1982-01-01

New matrix elements for the Airy continuum basis are employed to find quantum mechanical dissociation probabilities for the the forced Morse oscillator. The calculations performed illustrate the ease with which the continuously infinite Airy basis can be manipulated, and they illustrate the transition from vibrational enhancement to vibrational inhibition of diatomic breakup. The forced Morse oscillator model thus reproduces the behavior of more complicated collinear collision-induced dissociation systems

Dissociative Functions in the Normal Mourning Process.

Science.gov (United States)

Kauffman, Jeffrey

1994-01-01

Sees dissociative functions in mourning process as occurring in conjunction with integrative trends. Considers initial shock reaction in mourning as model of normal dissociation in mourning process. Dissociation is understood to be related to traumatic significance of death in human consciousness. Discerns four psychological categories of…

Dissociable brain signatures of choice conflict and immediate reward preferences in alcohol use disorders.

Science.gov (United States)

Amlung, Michael; Sweet, Lawrence H; Acker, John; Brown, Courtney L; MacKillop, James

2014-07-01

Impulsive delayed reward discounting (DRD) is an important behavioral process in alcohol use disorders (AUDs), reflecting incapacity to delay gratification. Recent work in neuroeconomics has begun to unravel the neural mechanisms supporting DRD, but applications of neuroeconomics in relation to AUDs have been limited. This study examined the neural mechanisms of DRD preferences in AUDs, with emphasis on dissociating activation patterns based on DRD choice type and level of cognitive conflict. Heavy drinking adult men with (n = 13) and without (n = 12) a diagnosis of an AUD completed a monetary DRD task during a functional magnetic resonance imaging scan. Participant responses were coded based on choice type (impulsive versus restrained) and level of cognitive conflict (easy versus hard). AUD+ participants exhibited significantly more impulsive DRD decision-making. Significant activation during DRD was found in several decision-making regions, including dorsolateral prefrontal cortex (DLPFC), insula, posterior parietal cortex (PPC), and posterior cingulate. An axis of cognitive conflict was also observed, with hard choices associated with anterior cingulate cortex and easy choices associated with activation in supplementary motor area. AUD+ individuals exhibited significant hyperactivity in regions associated with cognitive control (DLPFC) and prospective thought (PPC) and exhibited less task-related deactivation of areas associated with the brain's default network during DRD decisions. This study provides further clarification of the brain systems supporting DRD in general and in relation to AUDs. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

DEFF Research Database (Denmark)

Vinther, Kasper; Green, Torben; Østergaard, Søren

2017-01-01

This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

Differentiating neural systems mediating the acquisition versus expression of goal-directed and habitual behavioral control

Science.gov (United States)

Liljeholm, Mimi; Dunne, Simon; O'Doherty, John P.

2015-01-01

Considerable behavioral data indicates that operant actions can become habitual, as evidenced by insensitivity to changes in the action-outcome contingency and in subjective outcome values. Notably, although several studies have investigated the neural substrates of habits, none has clearly differentiated the areas of the human brain that support habit formation from those that implement habitual control. We scanned participants with fMRI as they learned and performed an operant task in which the conditional structure of the environment encouraged either goal-directed encoding of the consequences of actions, or a habit-like mapping of actions to antecedent cues. Participants were also scanned during a subsequent assessment of insensitivity to outcome devaluation. We identified dissociable roles of the cerebellum and ventral striatum, across learning and test performance, in behavioral insensitivity to outcome devaluation. We also show that the inferior parietal lobule – an area previously implicated in several aspects of goal-directed action selection, including the attribution of intent and awareness of agency – predicts sensitivity to outcome devaluation. Finally, we reveal a potential functional homology between the human subgenual cortex and rodent infralimbic cortex in the implementation of habitual control. In summary, our findings suggest a broad systems division, at the cortical and subcortical levels, between brain areas mediating the encoding and expression of action-outcome and stimulus-response associations. PMID:25892332

Malingering dissociative identity disorder: objective and projective assessment.

Science.gov (United States)

Labott, Susan M; Wallach, Heather R

2002-04-01

Verification of dissociative identity disorder presents challenges given the complex nature of the illness. This study addressed the concern that this disorder can be successfully malingered on objective and projective psychological tests. 50 undergraduate women were assigned to a Malingering or a Control condition, then completed the Rorschach Inkblot Test and the Dissociative Experiences Scale II. The Malingering group were asked to simulate dissociative identity disorder; controls received instructions to answer all materials honestly. Analysis indicated that malingerers were significantly more likely to endorse dissociative experiences on the Dissociative Experiences Scale II in the range common to patients with diagnosed dissociative identity disorder. However, on the Rorschach there were no significant differences between the two groups. Results suggest that the assessment of dissociative identity disorder requires a multifaceted approach with both objective and projective assessment tools. Research is needed to assess these issues in clinical populations.

Analysis of the DWPF glass pouring system using neural networks

International Nuclear Information System (INIS)

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

1997-01-01

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

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Timur Kartbayev

2017-03-01

Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

Fact or factitious? A psychobiological study of authentic and simulated dissociative identity states.

Science.gov (United States)

Reinders, A A T S; Reinders, A A T Simone; Willemsen, Antoon T M; Vos, Herry P J; den Boer, Johan A; Nijenhuis, Ellert R S

2012-01-01

Dissociative identity disorder (DID) is a disputed psychiatric disorder. Research findings and clinical observations suggest that DID involves an authentic mental disorder related to factors such as traumatization and disrupted attachment. A competing view indicates that DID is due to fantasy proneness, suggestibility, suggestion, and role-playing. Here we examine whether dissociative identity state-dependent psychobiological features in DID can be induced in high or low fantasy prone individuals by instructed and motivated role-playing, and suggestion. DID patients, high fantasy prone and low fantasy prone controls were studied in two different types of identity states (neutral and trauma-related) in an autobiographical memory script-driven (neutral or trauma-related) imagery paradigm. The controls were instructed to enact the two DID identity states. Twenty-nine subjects participated in the study: 11 patients with DID, 10 high fantasy prone DID simulating controls, and 8 low fantasy prone DID simulating controls. Autonomic and subjective reactions were obtained. Differences in psychophysiological and neural activation patterns were found between the DID patients and both high and low fantasy prone controls. That is, the identity states in DID were not convincingly enacted by DID simulating controls. Thus, important differences regarding regional cerebral bloodflow and psychophysiological responses for different types of identity states in patients with DID were upheld after controlling for DID simulation. The findings are at odds with the idea that differences among different types of dissociative identity states in DID can be explained by high fantasy proneness, motivated role-enactment, and suggestion. They indicate that DID does not have a sociocultural (e.g., iatrogenic) origin.

Sliding mode synchronization controller design with neural network for uncertain chaotic systems

Energy Technology Data Exchange (ETDEWEB)

Mou Chen [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)], E-mail: chenmou@nuaa.edu.cn; Jiang Changsheng; Bin Jiang; Wu Qingxian [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)

2009-02-28

A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

Three-body dissociations: The photodissociation of dimethyl sulfoxide at 193 nm

International Nuclear Information System (INIS)

Blank, D.A.; North, S.W.; Stranges, D.

1997-01-01

When a molecule with two equivalent chemical bonds is excited above the threshold for dissociation of both bonds, how the rupture of the two bonds is temporally coupled becomes a salient question. Following absorption at 193 nm dimethyl sulfoxide (CH 3 SOCH 3 ) contains enough energy to rupture both C-S bonds. This can happen in a stepwise (reaction 1) or concerted (reaction 2) fashion where the authors use rotation of the SOCH 3 intermediate prior to dissociation to define a stepwise dissociation: (1) CH 3 SOCH 3 → 2CH 3 + SO; (2a) CH 3 SOCH 3 → CH 3 + SOCH 3 ; and (2b) SOCH 3 → SO + CH 3 . Recently, the dissociation of dimethyl sulfoxide following absorption at 193 nm was suggested to involve simultaneous cleavage of both C-S bonds on an excited electronic surface. This conclusion was inferred from laser induced fluorescence (LIF) and resonant multiphoton ionization (2+1 REMPI) measurements of the internal energy content in the CH 3 and SO photoproducts and a near unity quantum yield measured for SO. Since this type of concerted three body dissociation is very interesting and a rather rare event in photodissociation dynamics, the authors chose to investigate this system using the technique of photofragment translational spectroscopy at beamline 9.0.2.1. The soft photoionization provided by the VUV undulator radiation allowed the authors to probe the SOCH 3 intermediate which had not been previously observed and provided good evidence that the dissociation of dimethyl sulfoxide primarily proceeds via a two step dissociation, reaction 2

Three-dimensional hydrogel cell culture systems for modeling neural tissue

Science.gov (United States)

Frampton, John

Two-dimensional (2-D) neural cell culture systems have served as physiological models for understanding the cellular and molecular events that underlie responses to physical and chemical stimuli, control sensory and motor function, and lead to the development of neurological diseases. However, the development of three-dimensional (3-D) cell culture systems will be essential for the advancement of experimental research in a variety of fields including tissue engineering, chemical transport and delivery, cell growth, and cell-cell communication. In 3-D cell culture, cells are provided with an environment similar to tissue, in which they are surrounded on all sides by other cells, structural molecules and adhesion ligands. Cells grown in 3-D culture systems display morphologies and functions more similar to those observed in vivo, and can be cultured in such a way as to recapitulate the structural organization and biological properties of tissue. This thesis describes a hydrogel-based culture system, capable of supporting the growth and function of several neural cell types in 3-D. Alginate hydrogels were characterized in terms of their biomechanical and biochemical properties and were functionalized by covalent attachment of whole proteins and peptide epitopes. Methods were developed for rapid cross-linking of alginate hydrogels, thus permitting the incorporation of cells into 3-D scaffolds without adversely affecting cell viability or function. A variety of neural cell types were tested including astrocytes, microglia, and neurons. Cells remained viable and functional for longer than two weeks in culture and displayed process outgrowth in 3-D. Cell constructs were created that varied in cell density, type and organization, providing experimental flexibility for studying cell interactions and behavior. In one set of experiments, 3-D glial-endothelial cell co-cultures were used to model blood-brain barrier (BBB) structure and function. This co-culture system was

ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

Energy Technology Data Exchange (ETDEWEB)

Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

1997-10-01

For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

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

Science.gov (United States)

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

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

Isotope exchange study of the dissociation of metal-humic substance complexes

International Nuclear Information System (INIS)

Mizera, J.; Jansova, A.; Hvozdova, I.; Benes, P.; Novak, F.

2003-01-01

Isotope exchange was employed to study dissociation of metal cations from their complexes with humic substances (HS). Dissociation of cation from HS controls the rate of isotope exchange between two identical metal-HS solutions (but for the presence of a radiotracer) divided by a dialysis membrane. The rate of isotope exchange of Eu/ 152 Eu and Co/ 60 Co in the systems with various HS was monitored as a function of pH, ionic strength, and the degree of HS loading with metal. The apparent rate of Eu-HS dissociation was found to be enhanced by decreasing pH, increasing ionic strength, and increasing metal loading. Co-HS dissociation was too fast to be followed by the method. For interpretation of the experimental kinetic data, the multiple first order law has been applied. Based on the results, a concept of HS as a mixture of two types of binding sites is discussed. (author)

Development of the disable software reporting system on the basis of the neural network

Science.gov (United States)

Gavrylenko, S.; Babenko, O.; Ignatova, E.

2018-04-01

The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

Dissociable neural processes during risky decision-making in individuals with Internet-gaming disorder

Directory of Open Access Journals (Sweden)

Lu Liu

2017-01-01

Full Text Available Risk-taking is purported to be central to addictive behaviors. However, for Internet gaming disorder (IGD, a condition conceptualized as a behavioral addiction, the neural processes underlying impaired decision-making (risk evaluation and outcome processing related to gains and losses have not been systematically investigated. Forty-one males with IGD and 27 healthy comparison (HC male participants were recruited, and the cups task was used to identify neural processes associated with gain- and loss-related risk- and outcome-processing in IGD. During risk evaluation, the IGD group, compared to the HC participants, showed weaker modulation for experienced risk within the bilateral dorsolateral prefrontal cortex (DLPFC (t = −4.07; t = −3.94; PFWE < 0.05 and inferior parietal lobule (IPL (t = −4.08; t = −4.08; PFWE < 0.05 for potential losses. The modulation of the left DLPFC and bilateral IPL activation were negatively related to addiction severity within the IGD group (r = −0.55; r = −0.61; r = −0.51; PFWE < 0.05. During outcome processing, the IGD group presented greater responses for the experienced reward within the ventral striatum, ventromedial prefrontal cortex, and orbitofrontal cortex (OFC (t = 5.04, PFWE < 0.05 for potential gains, as compared to HC participants. Within the IGD group, the increased reward-related activity in the right OFC was positively associated with severity of IGD (r = 0.51, PFWE < 0.05. These results provide a neurobiological foundation for decision-making deficits in individuals with IGD and suggest an imbalance between hypersensitivity for reward and weaker risk experience and self-control for loss. The findings suggest a biological mechanism for why individuals with IGD may persist in game-seeking behavior despite negative consequences, and treatment development strategies may focus on targeting these neural pathways in this population.

A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

International Nuclear Information System (INIS)

Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

2006-01-01

In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

Dynamic artificial neural networks with affective systems.

Directory of Open Access Journals (Sweden)

Catherine D Schuman

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

Filtering the reality: functional dissociation of lateral and medial pain systems during sleep in humans.

Science.gov (United States)

Bastuji, Hélène; Mazza, Stéphanie; Perchet, Caroline; Frot, Maud; Mauguière, François; Magnin, Michel; Garcia-Larrea, Luis

2012-11-01

Behavioral reactions to sensory stimuli during sleep are scarce despite preservation of sizeable cortical responses. To further understand such dissociation, we recorded intracortical field potentials to painful laser pulses in humans during waking and all-night sleep. Recordings were obtained from the three cortical structures receiving 95% of the spinothalamic cortical input in primates, namely the parietal operculum, posterior insula, and mid-anterior cingulate cortex. The dynamics of responses during sleep differed among cortical sites. In sleep Stage 2, evoked potential amplitudes were similarly attenuated relative to waking in all three cortical regions. During paradoxical, or rapid eye movements (REM), sleep, opercular and insular potentials remained stable in comparison with Stage 2, whereas the responses from mid-anterior cingulate abated drastically, and decreasing below background noise in half of the subjects. Thus, while the lateral operculo-insular system subserving sensory analysis of somatic stimuli remained active during paradoxical-REM sleep, mid-anterior cingulate processes related to orienting and avoidance behavior were suppressed. Dissociation between sensory and orienting-motor networks might explain why nociceptive stimuli can be either neglected or incorporated into dreams without awakening the subject. Copyright © 2011 Wiley Periodicals, Inc.

In Situ Representations and Access Consciousness in Neural Blackboard or Workspace Architectures

OpenAIRE

Frank van der Velde

2018-01-01

Phenomenal theories of consciousness assert that consciousness is based on specific neural correlates in the brain, which can be separated from all cognitive functions we can perform. If so, the search for robot consciousness seems to be doomed. By contrast, theories of functional or access consciousness assert that consciousness can be studied only with forms of cognitive access, given by cognitive processes. Consequently, consciousness and cognitive access cannot be fully dissociated. Here,...

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

Science.gov (United States)

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Prototype Learning and Dissociable Categorization Systems in Alzheimer’s Disease

Science.gov (United States)

Heindel, William C.; Festa, Elena K.; Ott, Brian R.; Landy, Kelly M.; Salmon, David P.

2015-01-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer’s disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of

The Remains of the Day in Dissociative Amnesia

Directory of Open Access Journals (Sweden)

Angelica Staniloiu

2012-04-01

Full Text Available Memory is not a unity, but is divided along a content axis and a time axis, respectively. Along the content dimension, five long-term memory systems are described, according to their hierarchical ontogenetic and phylogenetic organization. These memory systems are assumed to be accompanied by different levels of consciousness. While encoding is based on a hierarchical arrangement of memory systems from procedural to episodic-autobiographical memory, retrieval allows independence in the sense that no matter how information is encoded, it can be retrieved in any memory system. Thus, we illustrate the relations between various long-term memory systems by reviewing the spectrum of abnormalities in mnemonic processing that may arise in the dissociative amnesia—a condition that is usually characterized by a retrieval blockade of episodic-autobiographical memories and occurs in the context of psychological trauma, without evidence of brain damage on conventional structural imaging. Furthermore, we comment on the functions of implicit memories in guiding and even adaptively molding the behavior of patients with dissociative amnesia and preserving, in the absence of autonoetic consciousness, the so-called “internal coherence of life”.

Dissociative Spectrum Disorders in the Primary Care Setting

OpenAIRE

Elmore, James L.

2000-01-01

Dissociative disorders have a lifetime prevalence of about 10%. Dissociative symptoms may occur in acute stress disorder, posttraumatic stress disorder, somatization disorder, substance abuse, trance and possession trance, Ganser's syndrome, and dissociative identity disorder, as well as in mood disorders, psychoses, and personality disorders. Dissociative symptoms and disorders are observed frequently among patients attending our rural South Carolina community mental health center. Given the...

Dynamics of a neural system with a multiscale architecture

Science.gov (United States)

Breakspear, Michael; Stam, Cornelis J

2005-01-01

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

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Science.gov (United States)

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Application of neural networks to software quality modeling of a very large telecommunications system.

Science.gov (United States)

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

Hybrid information privacy system: integration of chaotic neural network and RSA coding

Science.gov (United States)

Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

2005-03-01

Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

Convergent paradigms for visual neuroscience and dissociative identity disorder.

Science.gov (United States)

Manning, Mark L; Manning, Rana L

2009-01-01

Although dissociative identity disorder, a condition in which multiple individuals appear to inhabit a single body, is a recognized psychiatric disorder, patients may yet encounter health professionals who declare that they simply "do not believe in multiple personalities." This article explores the proposal that resistance to the disorder represents a failure to apply an appropriate paradigm from which the disorder should be interpreted. Trauma and sociocognitive explanations of dissociative identity disorder are contrasted. The trauma hypothesis is further differentiated into paradigms in which trauma affects a defense mechanism, and one in which trauma serves to inhibit the normal integration sequence of parallel processes of the self in childhood. This latter paradigm is shown to be broadly consistent with current models of cortical processing in another system, the cortical visual system.

Neural systems supporting and affecting economically relevant behavior

Directory of Open Access Journals (Sweden)

Braeutigam S

2012-05-01

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

Rate coefficients for dissociative attachment and resonant electron-impact dissociation involving vibrationally excited O{sub 2} molecules

Energy Technology Data Exchange (ETDEWEB)

Laporta, V. [Istituto di Metodologie Inorganiche e dei Plasmi, CNR, Bari, Italy and Department of Physics and Astronomy, University College London, London WC1E 6BT (United Kingdom); Celiberto, R. [Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica, Politecnico di Bari, Italy and Istituto di Metodologie Inorganiche e dei Plasmi, CNR, Bari (Italy); Tennyson, J. [Department of Physics and Astronomy, University College London, London WC1E 6BT (United Kingdom)

2014-12-09

Rate coefficients for dissociative electron attachment and electron-impact dissociation processes, involving vibrationally excited molecular oxygen, are presented. Analytical fits of the calculated numerical data, useful in the applications, are also provided.

Hypnotic suggestibility, cognitive inhibition, and dissociation.

Science.gov (United States)

Dienes, Zoltán; Brown, Elizabeth; Hutton, Sam; Kirsch, Irving; Mazzoni, Giuliana; Wright, Daniel B

2009-12-01

We examined two potential correlates of hypnotic suggestibility: dissociation and cognitive inhibition. Dissociation is the foundation of two of the major theories of hypnosis and other theories commonly postulate that hypnotic responding is a result of attentional abilities (including inhibition). Participants were administered the Waterloo-Stanford Group Scale of Hypnotic Susceptibility, Form C. Under the guise of an unrelated study, 180 of these participants also completed: a version of the Dissociative Experiences Scale that is normally distributed in non-clinical populations; a latent inhibition task, a spatial negative priming task, and a memory task designed to measure negative priming. The data ruled out even moderate correlations between hypnotic suggestibility and all the measures of dissociation and cognitive inhibition overall, though they also indicated gender differences. The results are a challenge for existing theories of hypnosis.

Dissociation, shame, complex PTSD, child maltreatment and intimate relationship self-concept in dissociative disorder, chronic PTSD and mixed psychiatric groups.

Science.gov (United States)

Dorahy, Martin J; Middleton, Warwick; Seager, Lenaire; McGurrin, Patrick; Williams, Mary; Chambers, Ron

2015-02-01

Whilst a growing body of research has examined dissociation and other psychiatric symptoms in severe dissociative disorders (DDs), there has been no systematic examination of shame and sense of self in relationships in DDs. Chronic child abuse often associated with severe DDs, like dissociative identity disorder, is likely to heighten shame and relationship concerns. This study investigated complex posttraumatic stress disorder (PTSD), borderline and Schneiderian symptoms, dissociation, shame, child abuse, and various markers of self in relationships (e.g., relationship esteem, relationship depression, fear of relationships). Participants were assessed via clinical interview with psychometrically sound questionnaires. They fell into three diagnostic groups, dissociative disorder (n=39; primarily dissociative identity disorder), chronic PTSD (Chr-PTSD; n=13) or mixed psychiatric presentations (MP; n=21; primarily mood and anxiety disorders). All participants had a history of child abuse and/or neglect, and the groups did not differ on age and gender. The DD group was higher on nearly all measured variables than the MP group, and had more severe dissociative, borderline and Schneiderian symptoms than the Chr-PTSD sample. Shame and complex PTSD symptoms fell marginally short of predicting reductions in relationship esteem, pathological dissociative symptoms predicted increased relationship depression, and complex PTSD symptoms predicted fear of relationships. The representativeness of the samples was unknown. Severe psychiatric symptoms differentiate DDs from chronic PTSD, while dissociation and shame have a meaningful impact on specific markers of relationship functioning in psychiatric patients with a history of child abuse and neglect. Copyright © 2014 Elsevier B.V. All rights reserved.

VUV Study of Electron-Pyrimidine Dissociative Excitation

Science.gov (United States)

Hein, Jeff; Al-Khazraji, Hajar; Tiessen, Collin; Lukic, Dragan; Trocchi, Joshuah; McConkey, William

2013-05-01

A crossed electron-gas beam system coupled to a VUV spectrometer has been used to investigate the dissociation of pyrimidine (C4H4N2) into excited atomic fragments in the electron-impact energy range from threshold to 375 eV. Data have been made absolute using Lyman- α from H2 as a secondary standard. The main features in the spectrum are the H Lyman series lines. The emission cross section of Lyman- α is measured to be (2.44 +/- 0.25) 10-18 cm2 at 100 eV impact energy. The probability of extracting C or N atoms from the ring is shown to be very small. Possible dissociation channels and excitation mechanisms in the parent molecule will be discussed. The authors thank NSERC (Canada) for financial support.

Zero-Point Energy Constraint for Unimolecular Dissociation Reactions. Giving Trajectories Multiple Chances To Dissociate Correctly.

Science.gov (United States)

Paul, Amit K; Hase, William L

2016-01-28

A zero-point energy (ZPE) constraint model is proposed for classical trajectory simulations of unimolecular decomposition and applied to CH4* → H + CH3 decomposition. With this model trajectories are not allowed to dissociate unless they have ZPE in the CH3 product. If not, they are returned to the CH4* region of phase space and, if necessary, given additional opportunities to dissociate with ZPE. The lifetime for dissociation of an individual trajectory is the time it takes to dissociate with ZPE in CH3, including multiple possible returns to CH4*. With this ZPE constraint the dissociation of CH4* is exponential in time as expected for intrinsic RRKM dynamics and the resulting rate constant is in good agreement with the harmonic quantum value of RRKM theory. In contrast, a model that discards trajectories without ZPE in the reaction products gives a CH4* → H + CH3 rate constant that agrees with the classical and not quantum RRKM value. The rate constant for the purely classical simulation indicates that anharmonicity may be important and the rate constant from the ZPE constrained classical trajectory simulation may not represent the complete anharmonicity of the RRKM quantum dynamics. The ZPE constraint model proposed here is compared with previous models for restricting ZPE flow in intramolecular dynamics, and connecting product and reactant/product quantum energy levels in chemical dynamics simulations.

Mirror Writing and a Dissociative Identity Disorder

Directory of Open Access Journals (Sweden)

Catherine Le

2009-01-01

Full Text Available Individuals with dissociative identity disorder (DID have been known to show varied skills and talents as they change from one dissociative state to another. For example, case reports have described people who have changed their handedness or have spoken foreign languages during their dissociative states. During an interview with a patient with DID, a surprising talent emerged when she wrote a sentence for the Folstein Mini-Mental State Exam—mirror writing. It is not known whether her mirror writing had a deeper level of meaning; however, it does emphasize the idiosyncratic nature of dissociative identity disorder.

Mirror writing and a dissociative identity disorder.

Science.gov (United States)

Le, Catherine; Smith, Joyce; Cohen, Lewis

2009-01-01

Individuals with dissociative identity disorder (DID) have been known to show varied skills and talents as they change from one dissociative state to another. For example, case reports have described people who have changed their handedness or have spoken foreign languages during their dissociative states. During an interview with a patient with DID, a surprising talent emerged when she wrote a sentence for the Folstein Mini-Mental State Exam-mirror writing. It is not known whether her mirror writing had a deeper level of meaning; however, it does emphasize the idiosyncratic nature of dissociative identity disorder.

Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

Science.gov (United States)

Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

2015-03-01

Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

Developmental dyslexia in Chinese and English populations: dissociating the effect of dyslexia from language differences

Science.gov (United States)

Hu, Wei; Lee, Hwee Ling; Zhang, Qiang; Liu, Tao; Geng, Li Bo; Seghier, Mohamed L.; Shakeshaft, Clare; Twomey, Tae; Green, David W.; Yang, Yi Ming

2010-01-01

Previous neuroimaging studies have suggested that developmental dyslexia has a different neural basis in Chinese and English populations because of known differences in the processing demands of the Chinese and English writing systems. Here, using functional magnetic resonance imaging, we provide the first direct statistically based investigation into how the effect of dyslexia on brain activation is influenced by the Chinese and English writing systems. Brain activation for semantic decisions on written words was compared in English dyslexics, Chinese dyslexics, English normal readers and Chinese normal readers, while controlling for all other experimental parameters. By investigating the effects of dyslexia and language in one study, we show common activation in Chinese and English dyslexics despite different activation in Chinese versus English normal readers. The effect of dyslexia in both languages was observed as less than normal activation in the left angular gyrus and in left middle frontal, posterior temporal and occipitotemporal regions. Differences in Chinese and English normal reading were observed as increased activation for Chinese relative to English in the left inferior frontal sulcus; and increased activation for English relative to Chinese in the left posterior superior temporal sulcus. These cultural differences were not observed in dyslexics who activated both left inferior frontal sulcus and left posterior superior temporal sulcus, consistent with the use of culturally independent strategies when reading is less efficient. By dissociating the effect of dyslexia from differences in Chinese and English normal reading, our results reconcile brain activation results with a substantial body of behavioural studies showing commonalities in the cognitive manifestation of dyslexia in Chinese and English populations. They also demonstrate the influence of cognitive ability and learning environment on a common neural system for reading. PMID:20488886

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

Science.gov (United States)

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

2017-01-01

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

Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

Directory of Open Access Journals (Sweden)

Xin Zhang

2014-01-01

Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

Science.gov (United States)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Sequential neural processes in abacus mental addition: an EEG and FMRI case study.

Science.gov (United States)

Ku, Yixuan; Hong, Bo; Zhou, Wenjing; Bodner, Mark; Zhou, Yong-Di

2012-01-01

Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.

A neural network method for solving a system of linear variational inequalities

International Nuclear Information System (INIS)

Lan Hengyou; Cui Yishun

2009-01-01

In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

Three-body dissociations: The photodissociation of dimethyl sulfoxide at 193 nm

Energy Technology Data Exchange (ETDEWEB)

Blank, D.A.; North, S.W.; Stranges, D. [Lawrence Berkeley National Lab., CA (United States)] [and others

1997-04-01

When a molecule with two equivalent chemical bonds is excited above the threshold for dissociation of both bonds, how the rupture of the two bonds is temporally coupled becomes a salient question. Following absorption at 193 nm dimethyl sulfoxide (CH{sub 3}SOCH{sub 3}) contains enough energy to rupture both C-S bonds. This can happen in a stepwise (reaction 1) or concerted (reaction 2) fashion where the authors use rotation of the SOCH{sub 3} intermediate prior to dissociation to define a stepwise dissociation: (1) CH{sub 3}SOCH{sub 3} {r_arrow} 2CH{sub 3} + SO; (2a) CH{sub 3}SOCH{sub 3} {r_arrow} CH{sub 3} + SOCH{sub 3}; and (2b) SOCH{sub 3} {r_arrow} SO + CH{sub 3}. Recently, the dissociation of dimethyl sulfoxide following absorption at 193 nm was suggested to involve simultaneous cleavage of both C-S bonds on an excited electronic surface. This conclusion was inferred from laser induced fluorescence (LIF) and resonant multiphoton ionization (2+1 REMPI) measurements of the internal energy content in the CH{sub 3} and SO photoproducts and a near unity quantum yield measured for SO. Since this type of concerted three body dissociation is very interesting and a rather rare event in photodissociation dynamics, the authors chose to investigate this system using the technique of photofragment translational spectroscopy at beamline 9.0.2.1. The soft photoionization provided by the VUV undulator radiation allowed the authors to probe the SOCH{sub 3} intermediate which had not been previously observed and provided good evidence that the dissociation of dimethyl sulfoxide primarily proceeds via a two step dissociation, reaction 2.

Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

Energy Technology Data Exchange (ETDEWEB)

Ondrej Linda; Milos Manic; Timothy R. McJunkin

2011-08-01

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

The role of neural networks in nuclear power plant safety systems

International Nuclear Information System (INIS)

Boger, Z.

1993-01-01

Neural networks (NN) techniques have been applied in recent years to many systems by researchers in the nuclear power industry, mainly for modeling and sensor validation. Recent results are reviewed, including new directions in applications to control systems, safety analysis, and ''virtual'' instruments. As new fast learning algorithms become available, large systems may be learned effectively, even with few training examples. The nuclear industry hesitates to include NN in safety related systems, but it seems that the obstacles could be overcome with the demonstration of successful applications, even from other industries. Coupling of full-scale reactor simulators, as fault database generators, with neural networks learning should be explored. The integration of Expert System technology with NN should improve the Validation and Verification tasks, and also help overcome psychological barriers. It may prove that the potential of NN to help operators, compared with the existing and proposed alternatives, outweigh the risks. (author). 58 refs, 2 figs

Systemic Case Formulation, Individualized Process Monitoring, and State Dynamics in a Case of Dissociative Identity Disorder.

Science.gov (United States)

Schiepek, Günter K; Stöger-Schmidinger, Barbara; Aichhorn, Wolfgang; Schöller, Helmut; Aas, Benjamin

2016-01-01

Objective: The aim of this case report is to demonstrate the feasibility of a systemic procedure (synergetic process management) including modeling of the idiographic psychological system and continuous high-frequency monitoring of change dynamics in a case of dissociative identity disorder. The psychotherapy was realized in a day treatment center with a female client diagnosed with borderline personality disorder (BPD) and dissociative identity disorder. Methods: A three hour long co-creative session at the beginning of the treatment period allowed for modeling the systemic network of the client's dynamics of cognitions, emotions, and behavior. The components (variables) of this idiographic system model (ISM) were used to create items for an individualized process questionnaire for the client. The questionnaire was administered daily through an internet-based monitoring tool (Synergetic Navigation System, SNS), to capture the client's individual change process continuously throughout the therapy and after-care period. The resulting time series were reflected by therapist and client in therapeutic feedback sessions. Results: For the client it was important to see how the personality states dominating her daily life were represented by her idiographic system model and how the transitions between each state could be explained and understood by the activating and inhibiting relations between the cognitive-emotional components of that system. Continuous monitoring of her cognitions, emotions, and behavior via SNS allowed for identification of important triggers, dynamic patterns, and psychological mechanisms behind seemingly erratic state fluctuations. These insights enabled a change in management of the dynamics and an intensified trauma-focused therapy. Conclusion: By making use of the systemic case formulation technique and subsequent daily online monitoring, client and therapist continuously refer to detailed visualizations of the mental and behavioral network and

Systemic Case Formulation, Individualized Process Monitoring, and State Dynamics in a Case of Dissociative Identity Disorder.

Directory of Open Access Journals (Sweden)

Guenter Karl Schiepek

2016-10-01

Full Text Available Objective. The aim of this case report is to demonstrate the feasibility of a systemic procedure (synergetic process management including modeling of the idiographic psychological system and continuous high-frequency monitoring of change dynamics in a case of dissociative identity disorder. The psychotherapy was realized in a day treatment center with a female client diagnosed with borderline personality disorder (BPD and dissociative identity disorder. Methods. A three hour long co-creative session at the beginning of the treatment period allowed for modeling the systemic network of the client’s dynamics of cognitions, emotions, and behavior. The components (variables of this idiographic system model (ISM were used to create items for an individualized process questionnaire for the client. The questionnaire was administered daily through an internet-based monitoring tool (Synergetic Navigation System, SNS, to capture the client’s individual change process continuously throughout the therapy and after-care period. The resulting time series were reflected by therapist and client in therapeutic feedback sessions. Results. For the client it was important to see how the personality states dominating her daily life were represented by her idiographic system model and how the transitions between each state could be explained and understood by the activating and inhibiting relations between the cognitive-emotional components of that system. Continuous monitoring of her cognitions, emotions, and behavior via SNS allowed for identification of important triggers, dynamic patterns, and psychological mechanisms behind seemingly erratic state fluctuations. These insights enabled a change in management of the dynamics and an intensified trauma-focused therapy. Conclusion. By making use of the systemic case formulation technique and subsequent daily online monitoring, client and therapist continuously refer to detailed visualizations of the mental and

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

Science.gov (United States)

Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

2017-12-21

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

Directory of Open Access Journals (Sweden)

Ahnsei Shon

2017-12-01

Full Text Available Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC-compliant power transmission circuit, a medical implant communication service (MICS-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

An artificial neural network for modeling reliability, availability and maintainability of a repairable system

International Nuclear Information System (INIS)

Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

2006-01-01

The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

Neural network application to aircraft control system design

Science.gov (United States)

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

1991-01-01

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

Neural network application to aircraft control system design

Science.gov (United States)

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

1991-01-01

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

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

A wireless transmission neural interface system for unconstrained non-human primates.

Science.gov (United States)

Fernandez-Leon, Jose A; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J; Hansen, Bryan J; Hu, Ming; Dragoi, Valentin

2015-10-01

Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

A wireless transmission neural interface system for unconstrained non-human primates

Science.gov (United States)

Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

2015-10-01

Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

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

Science.gov (United States)

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

2018-02-22

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

Unique insula subregion resting-state functional connectivity with amygdala complexes in posttraumatic stress disorder and its dissociative subtype.

Science.gov (United States)

Nicholson, Andrew A; Sapru, Iman; Densmore, Maria; Frewen, Paul A; Neufeld, Richard W J; Théberge, Jean; McKinnon, Margaret C; Lanius, Ruth A

2016-04-30

The insula and amygdala are implicated in the pathophysiology of posttraumatic stress disorder (PTSD), where both have been shown to be hyper/hypoactive in non-dissociative (PTSD-DS) and dissociative subtype (PTSD+DS) PTSD patients, respectively, during symptom provocation. However, the functional connectivity between individual insula subregions and the amygdala has not been investigated in persons with PTSD, with or without the dissociative subtype. We examined insula subregion (anterior, mid, and posterior) functional connectivity with the bilateral amygdala using a region-of-interest seed-based approach via PickAtlas and SPM8. Resting-state fMRI was conducted with (n=61) PTSD patients (n=44 PTSD-DS; n=17 PTSD+DS), and (n=40) age-matched healthy controls. When compared to controls, the PTSD-DS group displayed increased insula connectivity (bilateral anterior, bilateral mid, and left posterior) to basolateral amygdala clusters in both hemispheres, and the PTSD+DS group displayed increased insula connectivity (bilateral anterior, left mid, and left posterior) to the left basolateral amygdala complex. Moreover, as compared to PTSD-DS, increased insula subregion connectivity (bilateral anterior, left mid, and right posterior) to the left basolateral amygdala was found in PTSD+DS. Depersonalization/derealization symptoms and PTSD symptom severity correlated with insula subregion connectivity to the basolateral amygdala within PTSD patients. This study is an important first step in elucidating patterns of neural connectivity associated with unique symptoms of arousal/interoception, emotional processing, and awareness of bodily states, in PTSD and its dissociative subtype. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Molecular dynamics study of CO2 hydrate dissociation: Fluctuation-dissipation and non-equilibrium analysis.

Science.gov (United States)

English, Niall J; Clarke, Elaine T

2013-09-07

Equilibrium and non-equilibrium molecular dynamics (MD) simulations have been performed to investigate thermal-driven break-up of planar CO2 hydrate interfaces in liquid water at 300-320 K. Different guest compositions, at 85%, 95%, and 100% of maximum theoretical occupation, led to statistically-significant differences in the observed initial dissociation rates. The melting temperatures of each interface were estimated, and dissociation rates were observed to be strongly dependent on temperature, with higher dissociation rates at larger over-temperatures vis-à-vis melting. A simple coupled mass and heat transfer model developed previously was applied to fit the observed dissociation profiles, and this helps to identify clearly two distinct régimes of break-up; a second well-defined region is essentially independent of composition and temperature, in which the remaining nanoscale, de facto two-dimensional system's lattice framework is intrinsically unstable. From equilibrium MD of the two-phase systems at their melting point, the relaxation times of the auto-correlation functions of fluctuations in number of enclathrated guest molecules were used as a basis for comparison of the variation in the underlying, non-equilibrium, thermal-driven dissociation rates via Onsager's hypothesis, and statistically significant differences were found, confirming the value of a fluctuation-dissipation approach in this case.

General concept of a gas engine for a hybrid vehicle, operating on methanol dissociation products

International Nuclear Information System (INIS)

Tartakovsky, L.; Aleinikov, Y.; Fainberg, V.; Garbar, A.; Gutman, M.; Hetsroni, G.; Schindler, Y.; Zvirin, Y.

1998-01-01

The paper presents a general concept of a hybrid propulsion system, based on an SI internal combustion engine fueled by methanol dissociation products (MDP). The proposed hybrid propulsion scheme is a series hybrid, which allows the engine to be operated in an on-off mode at constant optimal regime. The engine is fed by gaseous products of methanol dissociation (mainly hydrogen and carbon monoxide) emerging from an on-board catalytic reformer. The general scheme and base operation features of the propulsion system are described. The benefits that may be achieved by combining the well-known idea of on-board methanol dissociation with the hybrid vehicle concept are discussed. The proposed scheme is compared with those of systems operating on gasoline, liquid methanol, hydrogen and also with the multi-regime (not hybrid) engine fed by MDP

Symmetry Breakdown in Ground State Dissociation of HD+

International Nuclear Information System (INIS)

Ben-Itzhak, I.; Wells, E.; Carnes, K. D.; Krishnamurthi, Vidhya; Weaver, O. L.; Esry, B. D.

2000-01-01

Experimental studies of the dissociation of the electronic ground state of HD + following ionization of HD by fast proton impact indicate that the H + +D 1s dissociation channel is more likely than the H1s+D + dissociation channel by about 7% . This isotopic symmetry breakdown is due to the finite nuclear mass correction to the Born-Oppenheimer approximation which makes the 1sσ state 3.7 meV lower than the 2pσ state at the dissociation limit. The measured fractions of the two dissociation channels are in agreement with coupled-channels calculations of 1sσ to 2pσ transitions. (c) 2000 The American Physical Society

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

Science.gov (United States)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

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

Directory of Open Access Journals (Sweden)

Bahita Mohamed

2011-01-01

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

Using vibrational Cooper minima to determine strong-field molecular-dissociation pathways

Science.gov (United States)

Severt, T.; Zohrabi, M.; Armstrong, G. S. J.; McKenna, J.; Gaire, B.; Kling, Nora G.; Ablikim, U.; Carnes, K. D.; Esry, B. D.; Ben-Itzhak, I.

2015-05-01

We explore the possibility of using vibrational ``Cooper minima'' (VCM) locations as a method to determine dissociation pathways of molecules in a strong laser field. As a test case, we study the laser-induced dissociation of an O2+ion beam by several wavelengths (λ = 800 , 400, and 266 nm) using a coincidence three-dimensional momentum imaging technique. Vibrational structure is observed in the kinetic energy release spectra, revealing a suppression of the dissociation of certain vibrational levels, which is a manifestation of the VCM effect. Previously, it has been shown in H2+that first-order time-dependent perturbation theory can be used to predict the locations of the VCM. We explore if the VCM locations predicted by perturbation theory can help uniquely identify dissociation pathways in O2+and consider its utility for other systems. Supported by the Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy. TS was partially supported by NSF-REU under Grant No. PHY-0851599.

Assessment of dissociation in Bosnian treatment-seeking refugees in Denmark.

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Palic, Sabina; Carlsson, Jessica; Armour, Cherie; Elklit, Ask

2015-05-01

Dissociative experiences are common in traumatized individuals, and can sometimes be mistaken for psychosis. It is difficult to identify pathological dissociation in the treatment of traumatized refugees, because there is a lack of systematic clinical descriptions of dissociative phenomena in refugees. Furthermore, we are currently unaware of how dissociation measures perform in this clinical group. To describe the phenomenology of dissociative symptoms in Bosnian treatment-seeking refugees in Denmark. As a part of a larger study, dissociation was assessed systematically in 86 Bosnian treatment-seeking refugees using a semi-structured clinical interview (Structured Interview for Disorders of Extreme Stress-dissociation subscale; SIDES-D) and a self-report scale (Dissociative Experiences Scale; DES). The SIDES-D indicated twice as high prevalence of pathological dissociation as the DES. According to the DES, 30% of the refugees had pathological dissociation 15 years after their resettlement. On the SIDES-D, depersonalization and derealization experiences were the most common. Also, questions about depersonalization and derealization at times elicited reporting of visual and perceptual hallucinations, which were unrelated to traumatic re-experiencing. Questions about personality alteration elicited spontaneous reports of a phenomenon of "split" pre- and post-war identity in the refugee group. Whether this in fact is a dissociative phenomenon, characteristic of severe traumatization in adulthood, needs further examination. Knowledge of dissociative symptoms in traumatized refugees is important in clinical settings to prevent misclassification and to better target psychotherapeutic interventions. Much development in the measurement of dissociation in refugees is needed.

Neural net based determination of generator-shedding requirements in electric power systems

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

1992-09-01

This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

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Podolski, Ina; Rettberg, Achim

To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

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Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

2014-09-01

Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

Neural network based system for script identification in Indian ...

Indian Academy of Sciences (India)

2016-08-26

Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

Fact or factitious? A psychobiological study of authentic and simulated dissociative identity states.

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A A T S Reinders

Full Text Available BACKGROUND: Dissociative identity disorder (DID is a disputed psychiatric disorder. Research findings and clinical observations suggest that DID involves an authentic mental disorder related to factors such as traumatization and disrupted attachment. A competing view indicates that DID is due to fantasy proneness, suggestibility, suggestion, and role-playing. Here we examine whether dissociative identity state-dependent psychobiological features in DID can be induced in high or low fantasy prone individuals by instructed and motivated role-playing, and suggestion. METHODOLOGY/PRINCIPAL FINDINGS: DID patients, high fantasy prone and low fantasy prone controls were studied in two different types of identity states (neutral and trauma-related in an autobiographical memory script-driven (neutral or trauma-related imagery paradigm. The controls were instructed to enact the two DID identity states. Twenty-nine subjects participated in the study: 11 patients with DID, 10 high fantasy prone DID simulating controls, and 8 low fantasy prone DID simulating controls. Autonomic and subjective reactions were obtained. Differences in psychophysiological and neural activation patterns were found between the DID patients and both high and low fantasy prone controls. That is, the identity states in DID were not convincingly enacted by DID simulating controls. Thus, important differences regarding regional cerebral bloodflow and psychophysiological responses for different types of identity states in patients with DID were upheld after controlling for DID simulation. CONCLUSIONS/SIGNIFICANCE: The findings are at odds with the idea that differences among different types of dissociative identity states in DID can be explained by high fantasy proneness, motivated role-enactment, and suggestion. They indicate that DID does not have a sociocultural (e.g., iatrogenic origin.

Fact or Factitious? A Psychobiological Study of Authentic and Simulated Dissociative Identity States

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Simone Reinders, A. A. T.; Willemsen, Antoon T. M.; Vos, Herry P. J.; den Boer, Johan A.; Nijenhuis, Ellert R. S.

2012-01-01

Background Dissociative identity disorder (DID) is a disputed psychiatric disorder. Research findings and clinical observations suggest that DID involves an authentic mental disorder related to factors such as traumatization and disrupted attachment. A competing view indicates that DID is due to fantasy proneness, suggestibility, suggestion, and role-playing. Here we examine whether dissociative identity state-dependent psychobiological features in DID can be induced in high or low fantasy prone individuals by instructed and motivated role-playing, and suggestion. Methodology/Principal Findings DID patients, high fantasy prone and low fantasy prone controls were studied in two different types of identity states (neutral and trauma-related) in an autobiographical memory script-driven (neutral or trauma-related) imagery paradigm. The controls were instructed to enact the two DID identity states. Twenty-nine subjects participated in the study: 11 patients with DID, 10 high fantasy prone DID simulating controls, and 8 low fantasy prone DID simulating controls. Autonomic and subjective reactions were obtained. Differences in psychophysiological and neural activation patterns were found between the DID patients and both high and low fantasy prone controls. That is, the identity states in DID were not convincingly enacted by DID simulating controls. Thus, important differences regarding regional cerebral bloodflow and psychophysiological responses for different types of identity states in patients with DID were upheld after controlling for DID simulation. Conclusions/Significance The findings are at odds with the idea that differences among different types of dissociative identity states in DID can be explained by high fantasy proneness, motivated role-enactment, and suggestion. They indicate that DID does not have a sociocultural (e.g., iatrogenic) origin. PMID:22768068

Double dissociation between rules and memory in music: an event-related potential study.

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Miranda, Robbin A; Ullman, Michael T

2007-11-01

Language and music share a number of characteristics. Crucially, both domains depend on both rules and memorized representations. Double dissociations between the neurocognition of rule-governed and memory-based knowledge have been found in language but not music. Here, the neural bases of both of these aspects of music were examined with an event-related potential (ERP) study of note violations in melodies. Rule-only violations consisted of out-of-key deviant notes that violated tonal harmony rules in novel (unfamiliar) melodies. Memory-only violations consisted of in-key deviant notes in familiar well-known melodies; these notes followed musical rules but deviated from the actual melodies. Finally, out-of-key notes in familiar well-known melodies constituted violations of both rules and memory. All three conditions were presented, within-subjects, to healthy young adults, half musicians and half non-musicians. The results revealed a double dissociation, independent of musical training, between rules and memory: both rule violation conditions, but not the memory-only violations, elicited an early, somewhat right-lateralized anterior-central negativity (ERAN), consistent with previous studies of rule violations in music, and analogous to the early left-lateralized anterior negativities elicited by rule violations in language. In contrast, both memory violation conditions, but not the rule-only violation, elicited a posterior negativity that might be characterized as an N400, an ERP component that depends, at least in part, on the processing of representations stored in long-term memory, both in language and in other domains. The results suggest that the neurocognitive rule/memory dissociation extends from language to music, further strengthening the similarities between the two domains.

Vibrational relaxation and dissociation of D2(vj) on Cu(111)

International Nuclear Information System (INIS)

Cacciatore, M.; DeFelice, P.; Capitelli, M.

1992-01-01

The dissociative chemisorption of H 2 /D 2 with single crystal Cu surface has recently been the object of experimental and theoretical investigations. Here the authors present their results for the D 2 (vj)/Cu(111) system obtained within a semiclassical model developed for the interaction of molecules with non-rigid surfaces. The dissociation probability for D 2 in a specific initial (vj) state has been computed as a function of the impact energy and the surface temperature set to 300K. The quantum tunneling probability through the potential barriers has also been evaluated. The results show that the D 2 dissociation probability is smaller when compared to that of H 2 . The D 2 absorption probability, as well as the energy transferred to the surface phonons, is higher then that found for H 2

The Parenting Experiences of Mothers with Dissociative Disorders.

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Benjamin, Lynn R.; Benjamin, Robert; Rind, Bruce

1998-01-01

Presents a qualitative analysis of the experience of parenting of mothers with dissociative disorders. Using the mothers' words, describes how the five symptom areas of dissociation impeded their parenting efforts. Discusses the necessity of addressing parenting in the treatment of client-mothers with dissociative disorders. (Author/MKA)

A neurally inspired musical instrument classification system based upon the sound onset.

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Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

Directory of Open Access Journals (Sweden)

F. I. Younis

2001-01-01

Full Text Available An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required.

Dissociation and the Development of Psychopathology.

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Putnam, Frank W.; Trickett, Penelope K.

This paper reviews the research on dissociation and the development of psychopathology in children and adolescents. Definitions and dimensions of dissociation are addressed, noting its range from normative daydreaming to the extremes found in individuals with multiple personality disorder. Memory dysfunctions, disturbances of identity, passive…

Optimization of workflow scheduling in Utility Management System with hierarchical neural network

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Srdjan Vukmirovic

2011-08-01

Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

Neural Computations in a Dynamical System with Multiple Time Scales.

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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.

Electron Transfer Dissociation and Collision-Induced Dissociation of Underivatized Metallated Oligosaccharides

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Schaller-Duke, Ranelle M.; Bogala, Mallikharjuna R.; Cassady, Carolyn J.

2018-05-01

Electron transfer dissociation (ETD) and collision-induced dissociation (CID) were used to investigate underivatized, metal-cationized oligosaccharides formed via electrospray ionization (ESI). Reducing and non-reducing sugars were studied including the tetrasaccharides maltotetraose, 3α,4β,3α-galactotetraose, stachyose, nystose, and a heptasaccharide, maltoheptaose. Univalent alkali, divalent alkaline earth, divalent and trivalent transition metal ions, and a boron group trivalent metal ion were adducted to the non-permethylated oligosaccharides. ESI generated [M + Met]+, [M + 2Met]2+, [M + Met]2+, [M + Met - H]+, and [M + Met - 2H]+ most intensely along with low intensity nitrate adducts, depending on the metal and sugar ionized. The ability of these metal ions to produce oligosaccharide adduct ions by ESI had the general trend: Ca(II) > Mg(II) > Ni(II) > Co(II) > Zn(II) > Cu(II) > Na(I) > K(I) > Al(III) ≈ Fe(III) ≈ Cr(III). Although trivalent metals were utilized, no triply charged ions were formed. Metal cations allowed for high ESI signal intensity without permethylation. ETD and CID on [M + Met]2+ produced various glycosidic and cross-ring cleavages, with ETD producing more cross-ring and internal ions, which are useful for structural analysis. Product ion intensities varied based on glycosidic-bond linkage and identity of monosaccharide sub-unit, and metal adducts. ETD and CID showed high fragmentation efficiency, often with complete precursor dissociation, depending on the identity of the adducted metal ion. Loss of water was occasionally observed, but elimination of small neutral molecules was not prevalent. For both ETD and CID, [M + Co]2+ produced the most uniform structurally informative dissociation with all oligosaccharides studied. The ETD and CID spectra were complementary. [Figure not available: see fulltext.

Electron Transfer Dissociation and Collision-Induced Dissociation of Underivatized Metallated Oligosaccharides

Science.gov (United States)

Schaller-Duke, Ranelle M.; Bogala, Mallikharjuna R.; Cassady, Carolyn J.

2018-02-01

Electron transfer dissociation (ETD) and collision-induced dissociation (CID) were used to investigate underivatized, metal-cationized oligosaccharides formed via electrospray ionization (ESI). Reducing and non-reducing sugars were studied including the tetrasaccharides maltotetraose, 3α,4β,3α-galactotetraose, stachyose, nystose, and a heptasaccharide, maltoheptaose. Univalent alkali, divalent alkaline earth, divalent and trivalent transition metal ions, and a boron group trivalent metal ion were adducted to the non-permethylated oligosaccharides. ESI generated [M + Met]+, [M + 2Met]2+, [M + Met]2+, [M + Met - H]+, and [M + Met - 2H]+ most intensely along with low intensity nitrate adducts, depending on the metal and sugar ionized. The ability of these metal ions to produce oligosaccharide adduct ions by ESI had the general trend: Ca(II) > Mg(II) > Ni(II) > Co(II) > Zn(II) > Cu(II) > Na(I) > K(I) > Al(III) ≈ Fe(III) ≈ Cr(III). Although trivalent metals were utilized, no triply charged ions were formed. Metal cations allowed for high ESI signal intensity without permethylation. ETD and CID on [M + Met]2+ produced various glycosidic and cross-ring cleavages, with ETD producing more cross-ring and internal ions, which are useful for structural analysis. Product ion intensities varied based on glycosidic-bond linkage and identity of monosaccharide sub-unit, and metal adducts. ETD and CID showed high fragmentation efficiency, often with complete precursor dissociation, depending on the identity of the adducted metal ion. Loss of water was occasionally observed, but elimination of small neutral molecules was not prevalent. For both ETD and CID, [M + Co]2+ produced the most uniform structurally informative dissociation with all oligosaccharides studied. The ETD and CID spectra were complementary. [Figure not available: see fulltext.

Dissociative identity disorder: a controversial diagnosis.

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Gillig, Paulette Marie

2009-03-01

A brief description of the controversies surrounding the diagnosis of dissociative identity disorder is presented, followed by a discussion of the proposed similarities and differences between dissociative identity disorder and borderline personality disorder. The phenomenon of autohypnosis in the context of early childhood sexual trauma and disordered attachment is discussed, as is the meaning of alters or alternate personalities. The author describes recent neurosciences research that may relate the symptoms of dissociative identity disorder to demonstrable disordered attention and memory processes. A clinical description of a typical patient presentation is included, plus some recommendations for approaches to treatment.

Neural systems and hormones mediating attraction to infant and child faces

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Lizhu eLuo

2015-07-01

Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention andattractionto infant cues in both sexes.

Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

International Nuclear Information System (INIS)

Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

2007-01-01

Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

Assessing the structure and meaningfulness of the dissociative subtype of PTSD.

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Ross, Jana; Baník, Gabriel; Dědová, Mária; Mikulášková, Gabriela; Armour, Cherie

2018-01-01

Studies conducted in the USA, Canada and Denmark have supported the existence of the dissociative PTSD subtype, characterized primarily by symptoms of depersonalization and derealization. The current study aimed to examine the dissociative PTSD subtype in an Eastern European, predominantly female (83.16%) sample, using an extended set of dissociative symptoms. A latent profile analysis was applied to the PTSD and dissociation data from 689 trauma-exposed university students from Slovakia. Four latent profiles of varying PTSD and dissociation symptomatology were uncovered. They were named non-symptomatic, moderate PTSD, high PTSD and dissociative PTSD. The dissociative PTSD profile showed elevations on depersonalization and derealization, but also the alternative dissociative indicators of gaps in awareness and memory, sensory misperceptions and cognitive and behavioural re-experiencing. The core PTSD symptoms of 'memory impairment' and 'reckless or self-destructive behaviour' were also significantly elevated in the dissociative PTSD profile. Moreover, anxiety and anger predicted membership in the dissociative PTSD profile. The results provide support for the proposal that the dissociative PTSD subtype can be characterized by a variety of dissociative symptoms.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

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Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

The Many Faces of Dissociation: Opportunities for Innovative Research in Psychiatry

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