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Sample records for hypothalamic neural systems

  1. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

    Science.gov (United States)

    Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

    2017-10-01

    All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

  2. Hormonal regulation of the hypothalamic melanocortin system.

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    Kim, Jung D; Leyva, Stephanie; Diano, Sabrina

    2014-01-01

    Regulation of energy homeostasis is fundamental for life. In animal species and humans, the Central Nervous System (CNS) plays a critical role in such regulation by integrating peripheral signals and modulating behavior and the activity of peripheral organs. A precise interplay between CNS and peripheral signals is necessary for the regulation of food intake and energy expenditure in the maintenance of energy balance. Within the CNS, the hypothalamus is a critical center for monitoring, processing and responding to peripheral signals, including hormones such as ghrelin, leptin, and insulin. Once in the brain, peripheral signals regulate neuronal systems involved in the modulation of energy homeostasis. The main hypothalamic neuronal circuit in the regulation of energy metabolism is the melanocortin system. This review will give a summary of the most recent discoveries on the hormonal regulation of the hypothalamic melanocortin system in the control of energy homeostasis.

  3. Hormonal regulation of the hypothalamic melanocortin system

    Directory of Open Access Journals (Sweden)

    Jung Dae eKim

    2014-12-01

    Full Text Available Regulation of energy homeostasis is fundamental for life. In animal species and humans, the Central Nervous System (CNS plays a critical role in such regulation by integrating peripheral signals and modulating behavior and the activity of peripheral organs. A precise interplay between CNS and peripheral signals is necessary for the regulation of food intake and energy expenditure in the maintenance of energy balance. Within the CNS, the hypothalamus is a critical center for monitoring, processing and responding to peripheral signals, including hormones such as ghrelin, leptin and insulin. Once in the brain, peripheral signals regulate neuronal systems involved in the modulation of energy homeostasis. The main hypothalamic neuronal circuit in the regulation of energy metabolism is the melanocortin system. This review will give a summary of the most recent discoveries on the hormonal regulation of the hypothalamic melanocortin system in the control of energy homeostasis.

  4. Ontogeny of the hypothalamic neuropeptide Y system.

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    Grove, Kevin L; Smith, M Susan

    2003-06-01

    Early onset obesity and type II diabetes is rapidly becoming an epidemic, especially within the United States. This dramatic increase is likely due to many factors including both prenatal and postnatal environmental cues. The purpose of this review is to highlight some of the recent advances in our knowledge of the development of the hypothalamic circuits involved in the regulation of energy balance, with a focus on the neuropeptide Y (NPY) system. Unlike the adult rat, during the postnatal period NPY is transiently expressed in several hypothalamic regions, along with the expected expression within the arcuate nucleus (ARH). These transient populations of NPY neurons during the postnatal period may provide local NPY production to sustain the necessary energy intake during this critical growth phase. This may be physiologically important since ARH-NPY projections do not fully develop until the 3rd postnatal week. The significance of this ontogeny is that many peripheral metabolic signals have little effect of feeding prior to the development of the ARH projections. The essential questions now are whether prenatal and/or postnatal exposure to high levels of insulin or leptin during development can cause permanent changes in the function of hypothalamic circuits. It is vital to understand not only the natural development of the hypothalamic circuits that regulate energy homeostasis, but also their abnormal development caused by maternal and postnatal environmental cues. This will be pivotal for designing intervention and therapeutics to treat early onset obesity/type II diabetes, which may very well need to be different from those designed to prevent/treat adult onset obesity/type II diabetes.

  5. 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.

  6. Adolescent exposure to anabolic/androgenic steroids and the neurobiology of offensive aggression: a hypothalamic neural model based on findings in pubertal Syrian hamsters.

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    Melloni, Richard H; Ricci, Lesley A

    2010-06-01

    Considerable public attention has been focused on the issue of youth violence, particularly that associated with drug use. It is documented that anabolic steroid use by teenagers is associated with a higher incidence of aggressive behavior and serious violence, yet little is known about how these drugs produce the aggressive phenotype. Here we discuss work from our laboratory on the relationship between the development and activity of select neurotransmitter systems in the anterior hypothalamus and anabolic steroid-induced offensive aggression using pubertal male Syrian hamsters (Mesocricetus auratus) as an adolescent animal model, with the express goal of synthesizing these data into an cogent neural model of the developmental adaptations that may underlie anabolic steroid-induced aggressive behavior. Notably, alterations in each of the neural systems identified as important components of the anabolic steroid-induced aggressive response occurred in a sub-division of the anterior hypothalamic brain region we identified as the hamster equivalent of the latero-anterior hypothalamus, indicating that this sub-region of the hypothalamus is an important site of convergence for anabolic steroid-induced neural adaptations that precipitate offensive aggression. Based on these findings we present in this review a neural model to explain the neurochemical regulation of anabolic steroid-induced offensive aggression showing the hypothetical interaction between the arginine vasopressin, serotonin, dopamine, gamma-aminobutyric acid, and glutamate neural systems in the anterior hypothalamic brain region. Copyright 2009 Elsevier Inc. All rights reserved.

  7. Hypothalamic-Pituitary-Gonadal Endocrine System in the Hagfish

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    Masumi eNozaki

    2013-12-01

    Full Text Available The hypothalamic-pituitary system is considered to be a seminal event that emerged prior to or during the differentiation of the ancestral agnathans (jawless vertebrates. Hagfishes as one of the only two extant members of the class of agnathans are considered the most primitive vertebrate known, living or extinct. Accordingly, studies on their reproduction are important for understanding the evolution and phylogenetic aspects of the vertebrate reproductive endocrine system. In gnathostomes (jawed vertebrates, the hormones of the hypothalamus and pituitary have been extensively studied and shown to have well-defined roles in the control of reproduction. In hagfish, it was thought that they did not have the same neuroendocrine control of reproduction as gnathostomes, since it was not clear whether the hagfish pituitary gland contained tropic hormones of any kind. This review highlights the recent findings of the hypothalamic-pituitary-gonadal endocrine system in the hagfish. In contrast to gnathostomes that have two gonadotropins (GTH: luteinizing hormone and follicle-stimulating hormone, only one pituitary GTH has been identified in the hagfish. Immunohistochemical and functional studies confirmed that this hagfish GTH was significantly correlated with the developmental stages of the gonads and showed the presence of a steroid (estradiol feedback system at the hypothalamic-pituitary levels. Moreover, while the identity of hypothalamic gonadotropin releasing hormone (GnRH has not been determined, immunoreactive (ir GnRH has been shown in the hagfish brain including seasonal changes of ir-GnRH corresponding to gonadal reproductive stages. In addition, a hagfish PQRFamide peptide was identified and shown to stimulate the expression of hagfish GTH mRNA in the hagfish pituitary. These findings provide evidence that there are neuroendocrine-pituitary hormones that share common structure and functional features compared to later evolved vertebrates.

  8. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  9. 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.

  10. The ventral-hypothalamic input route: a common neural network for abstract cognition and sexuality.

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    Motofei, Ion G; Rowland, David L

    2014-02-01

    Classically, external receptors of the body transmit information from the environment to the cerebral cortex via the thalamus. This review explains and argues that only concrete external information is transmitted from peripheral receptors to the cortex via a thalamic route, while abstract and sexual external information are actually transmitted from peripheral receptors to the cortex through a cognitive hypothalamic route. Sexual function typically implies participation of two distinct partners, ensuring reproduction in many species including humans. Human sexual response involves participation of multiple (environmental, biological, psychological) kinds of stimuli and processing, so the understanding of sexual control and response supposes integration between the classical physiological mechanisms with the more complex processes of our 'mind'. Cognition and sexuality are two relational functions, which are dependent on concrete (colours, sounds, etc.) and/or abstract (gestures, facial expression, how you move, the way you say something seemingly trivial, etc.) environmental cues. Abstract cues are encoded independent of the specific object features of the stimuli, suggesting that such cues should be transmitted and interpreted within the brain through a system different than the classical thalamo-cortical network that operates on concrete (material) information. Indeed, data show that the cerebral cortex is capable of interpreting two distinct (concrete and abstract) formats of information via distinct and non-compatible brain areas. We expand upon this abstract-concrete dichotomy of the brain, positing that the two distinct cortical networks should be uploaded with distinct information from the environment via two distinct informational input routes. These two routes would be represented by the two distinct routes of the ascending reticular activating system (ARAS), namely the classical/dorsal thalamic input route for concrete information and the ventral

  11. The hypothalamic magnocellular neurosecretory system in developing rats

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    E Farina Lipari

    2009-12-01

    Full Text Available Studies concerning the development of the magnocellular system are scarce and discordant in literature. We carried out an immunohistochemical study on supraotic and paraventricular hypothalamic nuclei using antivasopressin and antioxytocin antibodies in developing rats between the 15th day of intrauterine life and the 6th day of postnatal life. In addition, we performed RT-PCR experiments to establish the stage at which these hormones appear and neurosecretory activity commences. The results showed that supraoptic and paraventricular nuclei appear, respectively, on the 16th and the 18th day of intrauterine life and both immediately synthetize vasopressin neurohormone. By contrast, synthesis of oxytocin takes place from the 2nd day after birth. Probably, these nuclei synthetize oxytocin in conjunction with the decline of placental maternal oxytocin.

  12. The orexin neuropeptide system: Physical activity and hypothalamic function throughout the aging process.

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    Anastasia N Zink

    2014-11-01

    Full Text Available There is a rising medical need for novel therapeutic targets of physical activity. Physical activity spans from spontaneous, low intensity movements to voluntary, high-intensity exercise. Regulation of spontaneous and voluntary movement is distributed over many brain areas and neural substrates, but the specific cellular and molecular mechanisms responsible for mediating overall activity levels are not well understood. The hypothalamus plays a central role in the control of physical activity, which is executed through coordination of multiple signaling systems, including the orexin neuropeptides. Orexin producing neurons integrate physiological and metabolic information to coordinate multiple behavioral states and modulate physical activity in response to the environment. This review is organized around three questions: (1 How do orexin peptides modulate physical activity? (2 What are the effects of aging and lifestyle choices on physical activity? (3 What are the effects of aging on hypothalamic function and the orexin peptides? Discussion of these questions will provide a summary of the current state of knowledge regarding hypothalamic orexin regulation of physical activity during aging and provide a platform on which to develop improved clinical outcomes in age-associated obesity and metabolic syndromes.

  13. A case of rapid-onset obesity with hypothalamic dysfunction, hypoventilation, autonomic dysregulation, and neural crest tumor: ROHHADNET syndrome.

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    Abaci, Ayhan; Catli, Gonul; Bayram, Erhan; Koroglu, Tolga; Olgun, Hatice Nur; Mutafoglu, Kamer; Hiz, Ayse Semra; Cakmakci, Handan; Bober, Ece

    2013-01-01

    Rapid-onset obesity with hypoventilation, hypothalamic dysfunction, and autonomic dysregulation (ROHHAD) is a rare disorder that mimics both common obesity and genetic obesity syndromes along with several endocrine disorders during early childhood. We aim to present the clinical features, laboratory and imaging results, and treatment outcomes of a patient with ROHHAD syndrome. In this case report, we describe a 26-month-old boy who was admitted to our emergency department with dyspnea and cyanosis and was suspected to have ROHHAD syndrome due to his rapid-onset obesity and alveolar hypoventilation. A thoracal and abdominal magnetic resonance imaging was performed to demonstrate a possible accompanying neural crest tumor and it provided a yet asymptomatic retroperitoneal ganglioneuroblastoma. Based on these findings, the patient was diagnosed as ROHHADNET syndrome. Because of the high prevalence of cardiorespiratory arrest and probability of accompanying tumors, early recognition of ROHHAD syndrome is important. To prevent presumptive mortality and morbidity, ROHHAD syndrome should be considered in all cases of rapid and early-onset obesity associated with hypothalamic-pituitary endocrine dysfunctions.

  14. Hypothalamic Sirt1 regulates food intake in a rodent model system.

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    Işin Cakir

    2009-12-01

    Full Text Available Sirt1 is an evolutionarily conserved NAD(+ dependent deacetylase involved in a wide range of processes including cellular differentiation, apoptosis, as well as metabolism, and aging. In this study, we investigated the role of hypothalamic Sirt1 in energy balance. Pharmacological inhibition or siRNA mediated knock down of hypothalamic Sirt1 showed to decrease food intake and body weight gain. Central administration of a specific melanocortin antagonist, SHU9119, reversed the anorectic effect of hypothalamic Sirt1 inhibition, suggesting that Sirt1 regulates food intake through the central melanocortin signaling. We also showed that fasting increases hypothalamic Sirt1 expression and decreases FoxO1 (Forkhead transcription factor acetylation suggesting that Sirt1 regulates the central melanocortin system in a FoxO1 dependent manner. In addition, hypothalamic Sirt1 showed to regulate S6K signaling such that inhibition of the fasting induced Sirt1 activity results in up-regulation of the S6K pathway. Thus, this is the first study providing a novel role for the hypothalamic Sirt1 in the regulation of food intake and body weight. Given the role of Sirt1 in several peripheral tissues and hypothalamus, potential therapies centered on Sirt1 regulation might provide promising therapies in the treatment of metabolic diseases including obesity.

  15. DiI tracing of the hypothalamic projection systems during perinatal development

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    Irina Georgievna Makarenko

    2014-12-01

    Full Text Available The hypothalamus is the higher neuroendocrine center of the brain and therefore possesses numerous intrinsic axonal connections and is connected by afferent and efferent fiber systems with other brain structures. These projection systems have been described in detail in the adult but data on their early development is sparse. Here I review studies of the time schedule and features of the development of the major hypothalamic axonal systems. In general, anterograde tracing experiments have been used to analyze short distance projections from the arcuate and anteroventral periventricular nuclei, while hypothalamic projections to the posterior and intermediate pituitary lobes and median eminence, mammillary body tracts and reciprocal septohypothalamic connections have been described with retrograde tracing. The available data demonstrate that hypothalamic connections develop with a high degree of spatial and temporal specificity, innervating each target with a unique developmental schedule which in many cases can be correlated with the functional maturity of the projection system.

  16. HYPOTHALAMIC NEUROHORMONES AND IMMUNE RESPONSES

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    J. Luis eQuintanar

    2013-08-01

    Full Text Available The aim of this review is to provide a comprehensive examination of the current literature describing the neural-immune interactions, with emphasis on the most recent findings of the effects of neurohormones on immune system. Particularly, the role of hypothalamic hormones such as Thyrotropin-releasing hormone, Corticotropin-releasing hormone and Gonadotropin-releasing hormone. In the past few years, interest has been raised in extrapituitary actions of these neurohormones due to their receptors have been found in many non-pituitary tissues. Also, the receptors are present in immune cells, suggesting an autocrine or paracrine role within the immune system. In general, these neurohormones have been reported to exert immunomodulatory effects on cell proliferation, immune mediators release and cell function. The implications of these findings in understanding the network of hypothalamic neuropeptides and immune system are discussed.

  17. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

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

  18. The hypothalamic- pituitary -adrenal -leptin axis and metabolic health: A systems approach to resilience, robustness and control

    NARCIS (Netherlands)

    Aschbacher, K.; Rodriguez-Fernandez, M.; Wietmarschen, H. van; Tomiyama, A.; Jain, S.; Epel, E.; Doyle III, F.J.; Greef, J. van der

    2014-01-01

    Glucocorticoids contribute to obesity and metabolic syndrome; however, the mechanisms are unclear, and prognostic measures are unavailable. A systems level understanding of the hypothalamic-pituitary-adrenal (HPA) -leptin axis may reveal novel insights. Eighteen obese premenopausal women provided

  19. Hypothalamic tumor

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    Hypothalamic glioma; Hypothalamus - tumor ... The exact cause of hypothalamic tumors is not known. It is likely that they result from a combination of genetic and environmental factors. In children, ...

  20. Role of hypothalamic melanocortin system in adaptation of food intake to food protein increase in mice.

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    Bruno Pillot

    Full Text Available The hypothalamic melanocortin system--the melanocortin receptor of type 4 (MC4R and its ligands: α-melanin-stimulating hormone (α-MSH, agonist, inducing hypophagia, and agouti-related protein (AgRP, antagonist, inducing hyperphagia--is considered to play a central role in the control of food intake. We tested its implication in the mediation of the hunger-curbing effects of protein-enriched diets (PED in mice. Whereas there was a 20% decrease in food intake in mice fed on the PED, compared to mice fed on an isocaloric starch-enriched diet, there was a paradoxical decrease in expression of the hypothalamic proopiomelanocortin gene, precursor of α-MSH, and increase in expression of the gene encoding AgRP. The hypophagia effect of PED took place in mice with invalidation of either MC4R or POMC, and was even strengthened in mice with ablation of the AgRP-expressing neurons. These data strongly suggest that the hypothalamic melanocortin system does not mediate the hunger-curbing effects induced by changes in the macronutrient composition of food. Rather, the role of this system might be to defend the body against the variations in food intake generated by the nutritional environment.

  1. Degenerate coding in neural systems.

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    Leonardo, Anthony

    2005-11-01

    When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.

  2. Memory Storage and Neural Systems.

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    Alkon, Daniel L.

    1989-01-01

    Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)

  3. Fetal alcohol programming of hypothalamic proopiomelanocortin system by epigenetic mechanisms and later life vulnerability to stress.

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    Bekdash, Rola; Zhang, Changqing; Sarkar, Dipak

    2014-09-01

    Hypothalamic proopiomelanocortin (POMC) neurons, one of the major regulators of the hypothalamic-pituitary-adrenal (HPA) axis, immune functions, and energy homeostasis, are vulnerable to the adverse effects of fetal alcohol exposure (FAE). These effects are manifested in POMC neurons by a decrease in Pomc gene expression, a decrement in the levels of its derived peptide β-endorphin and a dysregulation of the stress response in the adult offspring. The HPA axis is a major neuroendocrine system with pivotal physiological functions and mode of regulation. This system has been shown to be perturbed by prenatal alcohol exposure. It has been demonstrated that the perturbation of the HPA axis by FAE is long-lasting and is linked to molecular, neurophysiological, and behavioral changes in exposed individuals. Recently, we showed that the dysregulation of the POMC system function by FAE is induced by epigenetic mechanisms such as hypermethylation of Pomc gene promoter and an alteration in histone marks in POMC neurons. This developmental programming of the POMC system by FAE altered the transcriptome in POMC neurons and induced a hyperresponse to stress in adulthood. These long-lasting epigenetic changes influenced subsequent generations via the male germline. We also demonstrated that the epigenetic programming of the POMC system by FAE was reversed in adulthood with the application of the inhibitors of DNA methylation or histone modifications. Thus, prenatal environmental influences, such as alcohol exposure, could epigenetically modulate POMC neuronal circuits and function to shape adult behavioral patterns. Identifying specific epigenetic factors in hypothalamic POMC neurons that are modulated by fetal alcohol and target Pomc gene could be potentially useful for the development of new therapeutic approaches to treat stress-related diseases in patients with fetal alcohol spectrum disorders. Copyright © 2014 by the Research Society on Alcoholism.

  4. Hypothalamic inflammation: a double-edged sword to nutritional diseases

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    Cai, Dongsheng; Liu, Tiewen

    2015-01-01

    The hypothalamus is one of the master regulators of various physiological processes, including energy balance and nutrient metabolism. These regulatory functions are mediated by discrete hypothalamic regions that integrate metabolic sensing with neuroendocrine and neural controls of systemic physiology. Neurons and non-neuronal cells in these hypothalamic regions act supportively to execute metabolic regulations. Under conditions of brain and hypothalamic inflammation, which may result from overnutrition-induced intracellular stresses or disease-associated systemic inflammatory factors, extracellular and intracellular environments of hypothalamic cells are disrupted, leading to central metabolic dysregulations and various diseases. Recent research has begun to elucidate the effects of hypothalamic inflammation in causing diverse components of metabolic syndrome leading to diabetes and cardiovascular disease. These new understandings have provocatively expanded previous knowledge on the cachectic roles of brain inflammatory response in diseases, such as infections and cancers. This review describes the molecular and cellular characteristics of hypothalamic inflammation in metabolic syndrome and related diseases as opposed to cachectic diseases, and also discusses concepts and potential applications of inhibiting central/hypothalamic inflammation to treat nutritional diseases. PMID:22417140

  5. 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.

  6. The origins of the vertebrate hypothalamic-pituitary-gonadal (HPG) and hypothalamic-pituitary-thyroid (HPT) endocrine systems: new insights from lampreys.

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    Sower, Stacia A; Freamat, Mihael; Kavanaugh, Scott I

    2009-03-01

    The acquisition of a hypothalamic-pituitary axis was a seminal event in vertebrate evolution leading to the neuroendocrine control of many complex functions including growth, reproduction, osmoregulation, stress and metabolism. Lampreys as basal vertebrates are the earliest evolved vertebrates for which there are demonstrated functional roles for two gonadotropin-releasing hormones (GnRHs) that act via the hypothalamic-pituitary-gonadal axis controlling reproductive processes. With the availability of the lamprey genome, we have identified a novel GnRH form (lamprey GnRH-II) and a novel glycoprotein hormone receptor, lGpH-R II (thyroid-stimulating hormone-like receptor). Based on functional studies, in situ hybridization and phylogenetic analysis, we hypothesize that the newly identified lamprey GnRH-II is an ancestral GnRH to the vertebrate GnRHs. This finding opens a new understanding of the GnRH family and can help to delineate the evolution of the complex neuro/endocrine axis of reproduction. A second glycoprotein hormone receptor (lGpH-R II) was also identified in the sea lamprey. The existing data suggest the existence of a primitive, overlapping yet functional HPG and HPT endocrine systems in this organism, involving one possibly two pituitary glycoprotein hormones and two glycoprotein hormone receptors as opposed to three or four glycoprotein hormones interacting specifically with three receptors in gnathostomes. We hypothesize that the glycoprotein hormone/glycoprotein hormone receptor systems emerged as a link between the neuro-hormonal and peripheral control levels during the early stages of gnathostome divergence. The significance of the results obtained by analysis of the HPG/T axes in sea lamprey may transcend the limited scope of the corresponding physiological compartments by providing important clues in respect to the interplay between genome-wide events (duplications), coding sequence (mutation) and expression control level evolutionary mechanisms

  7. Systemic Effects of Hypothermia due to Hypothalamic Dysfunction after Resection of a Craniopharyngioma : Case Report and Review of Literature

    NARCIS (Netherlands)

    de Vetten, L.; Bocca, Gianni

    Objective With this case report, we aim to improve recognition of the systemic effects of hypothermia due to hypothalamic dysfunctioning. We present a patient who developed temperature dysregulation after surgery for craniopharyngioma. He suffered from several episodes of hypothermia associated with

  8. Nicotinic Cholinergic System in the Hypothalamus Modulates the Activity of the Hypothalamic Neuropeptides during the Stress Response

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    Balkan, Burcu; Pogun, Sakire

    2017-07-19

    The hypothalamus harbors high levels of cholinergic neurons and axon terminals. Nicotinic acetylcholine receptors, which play an important role in cholinergic neurotransmission, are expressed abundantly in the hypothalamus. Accumulating evidence reveals a regulatory role for nicotine in diverse functions mediated by the hypothalamus, including the regulation of the stress responses. The nicotinic cholinergic system lies at the intersection of homeostatic and reward pathways and shows sex differences in some of its effects. Furthermore, nicotinic acetylcholine receptor modulation offers significant potential for future drug development targeting pathologies related to hypothalamic functions. The present review will discuss the hypothalamic neuropeptides and their interaction with the nicotinic cholinergic system. The anatomical distribution of the cholinergic neurons, axon terminals and nicotinic receptors in discrete hypothalamic nuclei will be described. The effect of nicotinic cholinergic neurotransmission and nicotine exposure on HPA axis regulation at the hypothalamic level will be analyzed in view of the different neuropeptides involved. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. A newly identified mouse hypothalamic area having bidirectional neural connections with the lateral septum: the perifornical area of the anterior hypothalamus rich in chondroitin sulfate proteoglycans.

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    Horii-Hayashi, Noriko; Sasagawa, Takayo; Hashimoto, Takashi; Kaneko, Takeshi; Takeuchi, Kosei; Nishi, Mayumi

    2015-09-01

    While previous studies and brain atlases divide the hypothalamus into many nuclei and areas, uncharacterised regions remain. Here, we report a new region in the mouse anterior hypothalamus (AH), a triangular-shaped perifornical area of the anterior hypothalamus (PeFAH) between the paraventricular hypothalamic nucleus and fornix, that abundantly expresses chondroitin sulfate proteoglycans (CSPGs). The PeFAH strongly stained with markers for chondroitin sulfate/CSPGs such as Wisteria floribunda agglutinin and antibodies against aggrecan and chondroitin 6 sulfate. Nissl-stained sections of the PeFAH clearly distinguished it as a region of comparatively low density compared to neighboring regions, the paraventricular nucleus and central division of the anterior hypothalamic area. Immunohistochemical and DNA microarray analyses suggested that PeFAH contains sparsely distributed calretinin-positive neurons and a compact cluster of enkephalinergic neurons. Neuronal tract tracing revealed that both enkephalin- and calretinin-positive neurons project to the lateral septum (LS), while the PeFAH receives input from calbindin-positive LS neurons. These results suggest bidirectional connections between the PeFAH and LS. Considering neuronal subtype and projection, part of PeFAH that includes a cluster of enkephalinergic neurons is similar to the rat perifornical nucleus and guinea pig magnocellular dorsal nucleus. Finally, we examined c-Fos expression after several types of stimuli and found that PeFAH neuronal activity was increased by psychological but not homeostatic stressors. These findings suggest that the PeFAH is a source of enkephalin peptides in the LS and indicate that bidirectional neural connections between these regions may participate in controlling responses to psychological stressors. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  10. Early postnatal amylin treatment enhances hypothalamic leptin signaling and neural development in the selectively bred diet-induced obese rat.

    Science.gov (United States)

    Johnson, Miranda D; Bouret, Sebastien G; Dunn-Meynell, Ambrose A; Boyle, Christina N; Lutz, Thomas A; Levin, Barry E

    2016-12-01

    Selectively bred diet-induced obese (DIO) rats become obese on a high-fat diet and are leptin resistant before becoming obese. Compared with diet-resistant (DR) neonates, DIO neonates have impaired leptin-dependent arcuate (ARC) neuropeptide Y/agouti-related peptide (NPY/AgRP) and α-melanocyte-stimulating hormone (α-MSH; from proopiomelanocortin (POMC) neurons) axon outgrowth to the paraventricular nucleus (PVN). Using phosphorylation of STAT3 (pSTAT3) as a surrogate, we show that reduced DIO ARC leptin signaling develops by postnatal day 7 (P7) and is reduced within POMC but not NPY/AgRP neurons. Since amylin increases leptin signaling in adult rats, we treated DIO neonates with amylin during postnatal hypothalamic development and assessed leptin signaling, leptin-dependent ARC-PVN pathway development, and metabolic changes. DIO neonates treated with amylin from P0-6 and from P0-16 increased ARC leptin signaling and both AgRP and α-MSH ARC-PVN pathway development, but increased only POMC neuron number. Despite ARC-PVN pathway correction, P0-16 amylin-induced reductions in body weight did not persist beyond treatment cessation. Since amylin enhances adult DIO ARC signaling via an IL-6-dependent mechanism, we assessed ARC-PVN pathway competency in IL-6 knockout mice and found that the AgRP, but not the α-MSH, ARC-PVN pathway was reduced. These results suggest that both leptin and amylin are important neurotrophic factors for the postnatal development of the ARC-PVN pathway. Amylin might act as a direct neurotrophic factor in DIO rats to enhance both the number of POMC neurons and their α-MSH ARC-PVN pathway development. This suggests important and selective roles for amylin during ARC hypothalamic development.

  11. Impairment of inhibitory control of the hypothalamic pituitary adrenocortical system in epilepsy.

    Science.gov (United States)

    Zobel, Astrid; Wellmer, Jörg; Schulze-Rauschenbach, Svenja; Pfeiffer, Ute; Schnell, Susanne; Elger, Christian; Maier, Wolfgang

    2004-10-01

    Excess comorbidity between depression and epilepsy proposes common pathophysiological patterns in both disorders. Neuroendocrine abnormalities were often observed in depression as well as in epilepsy. Lack of inhibitory control of the hypothalamic pituitary adrenocortical (HPA) system is a core feature of depression; main relay stations of this system are located in the amygdala and hippocampus, which are key regions for both disorders. Therefore we explored the feedback mechanism of the HPA system in epilepsy. In order to control for the impact of depression we focused on epilepsies without depression. We compared patients with epilepsy (subdivided by medication with or without hepatic enzyme inducing antiepileptic medication) with 16 healthy controls and 16 patients with unipolar major depression but without epilepsy. We observed a lack of inhibitory control of the HPA system in patients with epilepsy, also in the absence of enzyme inducing medication. An impact of the temporal lobe location of the epileptic focus could not be observed. Thus, epilepsies share with depression the deficiencies in the feedback mechanism of the HPA system, proposing common pathophysiological features of up to now unknown nature.

  12. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  13. Hypothalamic-Pituitary-Adrenal Axis Modulation of Glucocorticoids in the Cardiovascular System

    Directory of Open Access Journals (Sweden)

    Natalie G. Burford

    2017-10-01

    Full Text Available The collective of endocrine organs acting in homeostatic regulation—known as the hypothalamic-pituitary-adrenal (HPA axis—comprises an integration of the central nervous system as well as peripheral tissues. These organs respond to imminent or perceived threats that elicit a stress response, primarily culminating in the release of glucocorticoids into the systemic circulation by the adrenal glands. Although the secretion of glucocorticoids serves to protect and maintain homeostasis in the typical operation at baseline levels, inadequate regulation can lead to physiologic and psychologic pathologies. The cardiovascular system is especially susceptible to prolonged dysregulation of the HPA axis and glucocorticoid production. There is debate about whether cardiovascular health risks arise from the direct detrimental effects of stress axis activation or whether pathologies develop secondary to the accompanying metabolic strain of excess glucocorticoids. In this review, we will explore the emerging research that indicates stress does have direct effects on the cardiovascular system via the HPA axis activation, with emphasis on the latest research on the impact of glucocorticoids signaling in the vasculature and the heart.

  14. Hypothalamic-Pituitary-Adrenal Axis Modulation of Glucocorticoids in the Cardiovascular System

    Science.gov (United States)

    Burford, Natalie G.; Webster, Natalia A.; Cruz-Topete, Diana

    2017-01-01

    The collective of endocrine organs acting in homeostatic regulation—known as the hypothalamic-pituitary-adrenal (HPA) axis—comprises an integration of the central nervous system as well as peripheral tissues. These organs respond to imminent or perceived threats that elicit a stress response, primarily culminating in the release of glucocorticoids into the systemic circulation by the adrenal glands. Although the secretion of glucocorticoids serves to protect and maintain homeostasis in the typical operation at baseline levels, inadequate regulation can lead to physiologic and psychologic pathologies. The cardiovascular system is especially susceptible to prolonged dysregulation of the HPA axis and glucocorticoid production. There is debate about whether cardiovascular health risks arise from the direct detrimental effects of stress axis activation or whether pathologies develop secondary to the accompanying metabolic strain of excess glucocorticoids. In this review, we will explore the emerging research that indicates stress does have direct effects on the cardiovascular system via the HPA axis activation, with emphasis on the latest research on the impact of glucocorticoids signaling in the vasculature and the heart. PMID:29035323

  15. Hypothalamic-Pituitary-Adrenal Axis Modulation of Glucocorticoids in the Cardiovascular System.

    Science.gov (United States)

    Burford, Natalie G; Webster, Natalia A; Cruz-Topete, Diana

    2017-10-16

    The collective of endocrine organs acting in homeostatic regulation-known as the hypothalamic-pituitary-adrenal (HPA) axis-comprises an integration of the central nervous system as well as peripheral tissues. These organs respond to imminent or perceived threats that elicit a stress response, primarily culminating in the release of glucocorticoids into the systemic circulation by the adrenal glands. Although the secretion of glucocorticoids serves to protect and maintain homeostasis in the typical operation at baseline levels, inadequate regulation can lead to physiologic and psychologic pathologies. The cardiovascular system is especially susceptible to prolonged dysregulation of the HPA axis and glucocorticoid production. There is debate about whether cardiovascular health risks arise from the direct detrimental effects of stress axis activation or whether pathologies develop secondary to the accompanying metabolic strain of excess glucocorticoids. In this review, we will explore the emerging research that indicates stress does have direct effects on the cardiovascular system via the HPA axis activation, with emphasis on the latest research on the impact of glucocorticoids signaling in the vasculature and the heart.

  16. Systemic Sympathoexcitation Was Associated with Paraventricular Hypothalamic Phosphorylation of Synaptic CaMKIIα and MAPK/ErK

    Directory of Open Access Journals (Sweden)

    Olalekan M. Ogundele

    2017-08-01

    Full Text Available Systemic administration of adrenergic agonist (Isoproterenol; ISOP is known to facilitate cardiovascular changes associated with heart failure through an upregulation of cardiac toll-like receptor 4 (TLR4. Furthermore, previous studies have shown that cardiac tissue-specific deletion of TLR4 protects the heart against such damage. Since the autonomic regulation of systemic cardiovascular function originates from pre-autonomic sympathetic centers in the brain, it is unclear how a systemically driven sympathetic change may affect the pre-autonomic paraventricular hypothalamic nuclei (PVN TLR4 expression. Here, we examined how change in PVN TLR4 was associated with alterations in the neurochemical cytoarchitecture of the PVN in systemic adrenergic activation. After 48 h of intraperitoneal 150 mg/kg ISOP treatment, there was a change in PVN CaMKIIα and MAPK/ErK expression, and an increase in TLR4 in expression. This was seen as an increase in p-MAPK/ErK, and a decrease in synaptic CaMKIIα expression in the PVN (p < 0.01 of ISOP treated mice. Furthermore, there was an upregulation of vesicular glutamate transporter (VGLUT 2; p < 0.01 and a decreased expression of GABA in the PVN of Isoproterenol (ISOP treated WT mice (p < 0.01. However, after a PVN-specific knockdown of TLR4, the effect of systemic administration of ISOP was attenuated, as indicated by a decrease in p-MAPK/ErK (p < 0.01 and upregulation of CaMKIIα (p < 0.05. Additionally, loss of inhibitory function was averted while VGLUT2 expression decreased when compared with the ISOP treated wild type mice and the control. Taken together, the outcome of this study showed that systemic adrenergic activation may alter the expression, and phosphorylation of preautonomic MAPK/ErK and CaMKIIα downstream of TLR4. As such, by outlining the roles of these kinases in synaptic function, we have identified the significance of neural TLR4 in the progression, and attenuation of synaptic changes in the

  17. THE COCHLEA AS AN INDEPENDENT NEUROENDOCRINE ORGAN: EXPRESSION AND POSSIBLE ROLES OF A LOCAL HYPOTHALAMIC-PITUITARY-ADRENAL AXIS-EQUIVALENT SIGNALING SYSTEM

    Science.gov (United States)

    Basappa, Johnvesly; Graham, Christine E.; Turcan, Sevin; Vetter, Douglas E.

    2012-01-01

    A key property possessed by the mammalian cochlea is its ability to dynamically alter its own sensitivity. Because hair cells and ganglion cells are prone to damage following exposure to loud sound, extant mechanisms limiting cochlear damage include modulation involving both the mechanical (via outer hair cell motility) and neural signaling (via inner hair cell-ganglion cell synapses) steps of peripheral auditory processing. Feedback systems such as that embodied by the olivocochlear system can alter sensitivity, but respond only after stimulus encoding, allowing potentially damaging sounds to impact the inner ear before sensitivity is adjusted. Less well characterized are potential cellular signaling systems involved in protection against metabolic stress and resultant damage. Although pharmacological manipulation of the olivocochlear system may hold some promise for attenuating cochlear damage, targeting this system may still allow damage to occur that does not depend on a fully functional feedback loop for its mitigation. Thus, understanding endogenous cell signaling systems involved in cochlear protection may lead to new strategies and therapies for prevention of cochlear damage and consequent hearing loss. We have recently discovered a novel cochlear signaling system that is molecularly equivalent to the classic hypothalamic-pituitary-adrenal (HPA) axis. This cochlear HPA-equivalent system functions to balance auditory sensitivity and susceptibility to noise-induced hearing loss, and also protects against cellular metabolic insults resulting from exposures to ototoxic drugs. This system may represent a local cellular response system designed to mitigate damage arising from various types of insult. PMID:22484018

  18. Hypothalamic pituitary dysfunction in acute nonmycobacterial infections of central nervous system

    Directory of Open Access Journals (Sweden)

    Dinesh K Dhanwal

    2011-01-01

    Full Text Available Background and Objective: Acute and chronic central nervous system (CNS infections are not uncommon in tropical countries and are associated with high morbidity and mortality if specific targeted therapy is not instituted in time. Effects of tubercular meningitis, a form of chronic meningitis on hypothalamic pituitary axis, are well known both at the time of diagnosis and after few months to years of illness. However, there are few reports of pituitary dysfunction in subjects with acute CNS infections. Therefore, this study was aimed at evaluating the pituitary hormonal profile in patients with nonmycobacterial acute meningitis at the time of presentation. Materials and Methods: This prospective case series study included 30 untreated adult patients with acute meningitis, meningoencephalitis, or encephalitis, due to various nonmycobacterial agents, admitted and registered with Lok Nayak Hospital, Maulana Aazd Medical College, New Delhi, between September 2007 and March 2009. Patients with preexisting endocrine diseases, tubercular meningitis and patients on steroids were carefully excluded from the study. The basal pituitary hormonal profile was measured by the electrochemilumniscence technique for serum cortisol, luetinizing hormone (LH, follicular stimulating hormone (FSH, prolactin (PRL, thyrotropin (TSH, free tri-iodothyronine (fT3, and free thyroxine (fT4. Results: The cases (n = 30 comprised of patients with acute pyogenic meningitis (n = 23, viral meningoencephalitis (n = 4, brain abscess (n = 2, and cryptococcal meningitis (n = 1. The mean age of patients was 28.97 ± 11.306 years. Out of 30 patients, 14 (46.7% were males and 16 (58.1% were females. Adrenal insufficiency both absolute and relative was seen in seven (23.3% and hyperprolactinemia was seen in nine (30.0% of the patients. One study subject had central hypothyroidism and seven (23.3 showed low levels of LH and/or FSH. None of patients showed clinical features suggestive of

  19. Novel in vitro perfusion system for the determination of hypothalamic-pituitary-adrenal axis responses.

    Science.gov (United States)

    Moidel, Melissa A; Belz, Emily E; Czambel, R Kenneth; Rubin, Robert T; Rhodes, Michael E

    2006-01-01

    The hypothalamic-pituitary-adrenal (HPA) axis is a three-gland component of the endocrine system and a key modulator of the stress response. We have developed a novel in vitro perfusion system to enable the study of pharmacological and hormonal challenges to tissue components of the HPA axis. In vivo studies have shown functional sex differences (sexual diergism) in HPA responses to cholinergic drugs, and in the present in vitro study, we examine these differences at several levels of the HPA axis. Hypothalami, pituitaries, and adrenal glands were collected from male and female rats (n=3 per sex). One-half hypothalamus, one-half pituitary, and one adrenal gland were placed individually into three Erlenmeyer flasks connected by tubing. Flasks were perfused with medium (pH 7.4) at 37 degrees C. Sampling ports between the flasks were used to collect buffer for determination of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) release from the hypothalamus, pituitary, and adrenal flasks, respectively, over an extended baseline period, to determine stability of the system, and after nicotine administration. The perfusion system produced steady CRH, ACTH, and CORT baselines, the ACTH and CORT values being comparable to in vivo basal ACTH and CORT values in jugular-vein-cannulated rats. In vitro CRH, ACTH, and CORT responses to nicotine were significantly increased at 10 min and returned to baseline by 30 min, the CRH and ACTH responses from female tissues being greater than responses from male tissues. These sex differences were similar to those following nicotine administration in vivo. The ability of this novel, dynamic in vitro system to replicate in vivo HPA axis responses supports its potential as a new method for pharmacological and toxicological studies.

  20. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

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

  1. Electroacupuncture regulate hypothalamic-pituitary-adrenal axis and enhance hippocampal serotonin system in a rat model of depression.

    Science.gov (United States)

    Le, Jing-jing; Yi, Tao; Qi, Li; Li, Ji; Shao, Lei; Dong, Jing-cheng

    2016-02-26

    Hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the pathogenesis of depression. Dysfunction of the hippocampal serotonin (5-hydroxytryptamine, 5-HT) system has been shown to be a key factor in depression. There is growing evidence that electro-acupuncture (EA) has antidepressant-like effect. However, the effect of EA on HPA axis and hippocampal serotonin system remains unknown. In our study, we investigated the antidepressant-like effect and mechanism of EA for depression rat models. Depression in rats was induced by chronic unpredictable mild stress (CUMS). EA treatment was administered once daily to CUMS rats for 14 days. The acupoints (ST36, bilateral and CV4) were selected. Untreated CUMS rats and normal rats were used as controls. Behavioral tests including forced swim test and open-field test were performed to evaluate the antidepressant effects of EA treatment. Hypothalamic corticotropin-releasing hormone (CRH) mRNA, plasma adrenocorticotropic hormone (ACTH) and corticosterone (CORT) were estimated as indices of HPA axis activity. Enzyme linked immunosorbent assay (ELISA) was performed to determine the concentrations of 5-HT in the hippocampus. Real-time PCR(RT-PCR)and Western blot were respectively used to detect the mRNA and protein levels of 5-hydroxytryptamine 1A receptor (5-HT1AR) in the hippocampus. Our results showed that EA treatment reversed the behavioral deficiency induced by CUMS in rats. EA treatment decreased CRH mRNA expression in the hypothalamic, and ACTH and CORT level in plasma, and markedly increased 5-HT concentration, 5-HT1AR (mRNA and protein) expression in the hippocampus. These results indicated that EA treatment could act on depression by modulating HPA axis and enhancing hippocampal 5-HT/5-HT1AR in CUMS Rats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. 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.

  3. 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.

  4. Neural Plasticity in the Gustatory System

    OpenAIRE

    Hill, David L.

    2004-01-01

    Sensory systems adapt to changing environmental influences by coordinated alterations in structure and function. These alterations are referred to as plastic changes. The gustatory system displays numerous plastic changes even in receptor cells. This review focuses on the plasticity of gustatory structures through the first synaptic relay in the brain. Unlike other sensory systems, there is a remarkable amount of environmentally induced changes in these peripheral-most neural structures. The ...

  5. 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.

  6. Unveiling neural coupling within the sensorimotor system : directionality and nonlinearity

    NARCIS (Netherlands)

    Yang, Y.; Dewald, J.P.A.; van der Helm, F.C.T.; Schouten, A.C.

    2017-01-01

    Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending

  7. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-12-01

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

  8. 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.

  9. Inhibition of reactive oxygen species in hypothalamic paraventricular nucleus attenuates the renin–angiotensin system and proinflammatory cytokines in hypertension

    Energy Technology Data Exchange (ETDEWEB)

    Su, Qing [Department of Physiology and Pathophysiology, Xi' an Jiaotong University Cardiovascular Research Center, Xi' an Jiaotong University School of Medicine, Xi' an 710061 (China); Qin, Da-Nian, E-mail: dnqin@stu.edu.cn [Department of Physiology, Shantou University Medical College, Shantou 515041 (China); Wang, Fu-Xin [Department of Neurology, The First Affiliated Hospital of Jiamusi University, Jiamusi 154002 (China); Ren, Jun [Center for Cardiovascular Research and Alternative Medicine, University of Wyoming, Laramie, WY 82071 (United States); Li, Hong-Bao; Zhang, Meng; Yang, Qing; Miao, Yu-Wang; Yu, Xiao-Jing; Qi, Jie [Department of Physiology and Pathophysiology, Xi' an Jiaotong University Cardiovascular Research Center, Xi' an Jiaotong University School of Medicine, Xi' an 710061 (China); Zhu, Zhiming [Department of Hypertension and Endocrinology, Center for Hypertension and Metabolic Diseases, Daping Hospital, The Third Military Medical University, Chongqing Institute of Hypertension, Chongqing 400042 (China); Zhu, Guo-Qing [Key Laboratory of Cardiovascular Disease and Molecular Intervention, Department of Physiology, Nanjing Medical University, Nanjing 210029 (China); Kang, Yu-Ming, E-mail: ykang@mail.xjtu.edu.cn [Department of Physiology and Pathophysiology, Xi' an Jiaotong University Cardiovascular Research Center, Xi' an Jiaotong University School of Medicine, Xi' an 710061 (China)

    2014-04-15

    Aims: To explore whether reactive oxygen species (ROS) scavenger (tempol) in the hypothalamic paraventricular nucleus (PVN) attenuates renin–angiotensin system (RAS) and proinflammatory cytokines (PICs), and decreases the blood pressure and sympathetic activity in angiotensin II (ANG II)-induced hypertension. Methods and results: Male Sprague–Dawley rats were infused intravenously with ANG II (10 ng/kg per min) or normal saline (NS) for 4 weeks. These rats were treated with bilateral PVN infusion of oxygen free radical scavenger tempol (TEMP, 20 μg/h) or vehicle (artificial cerebrospinal fluid, aCSF) for 4 weeks. ANG II infusion resulted in increased mean arterial pressure (MAP) and renal sympathetic nerve activity (RSNA). These ANG II-infused rats also had higher levels of gp91{sup phox} (a subunit of NAD(P)H oxidase), angiotensin-converting enzyme (ACE), and interleukin-1beta (IL-1β) in the PVN than the control animals. Treatment with PVN infusion of TEMP attenuated the overexpression of gp91{sup phox}, ACE and IL-1β within the PVN, and decreased sympathetic activity and MAP in ANG II-infused rats. Conclusion: These findings suggest that ANG II infusion induces elevated PICs and oxidative stress in the PVN, which contribute to the sympathoexcitation in hypertension. Inhibition of reactive oxygen species in hypothalamic paraventricular nucleus attenuates the renin–angiotensin system, proinflammatory cytokines and oxidative stress in ANG II-induced hypertension. - Highlights: • The effect of chronic inhibiting PVN superoxide on hypertension was investigated. • ANG II infusion induced increased proinflammatory cytokines and superoxide in PVN. • ANG II infusion resulted in oxidative stress, sympathoexcitation and hypertension. • Chronic inhibiting PVN superoxide attenuates RAS and cytokines in hypertension.

  10. The Artifical Neural Network as means for modeling Nonlinear Systems

    OpenAIRE

    Drábek Oldøich; Taufer Ivan

    1998-01-01

    The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  11. The Artifical Neural Network as means for modeling Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Drábek Oldøich

    1998-12-01

    Full Text Available The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  12. Neural systems and hormones mediating attraction to infant and child faces

    Directory of Open Access Journals (Sweden)

    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.

  13. Melanin-Concentrating Hormone acts through hypothalamic kappa opioid system and p70S6K to stimulate acute food intake.

    Science.gov (United States)

    Romero-Picó, Amparo; Sanchez-Rebordelo, Estrella; Imbernon, Monica; González-Touceda, David; Folgueira, Cintia; Senra, Ana; Fernø, Johan; Blouet, Clémence; Cabrera, Roberto; van Gestel, Margriet; Adan, Roger A; López, Miguel; Maldonado, Rafael; Nogueiras, Ruben; Diéguez, Carlos

    2018-03-01

    Melanin-Concentrating Hormone (MCH) is one of the most relevant orexigenic factors specifically located in the lateral hypothalamic area (LHA), with its physiological relevance demonstrated in studies using several genetically manipulated mice models. However, the central mechanisms controlling MCH-induced hyperphagia remain largely uncharacterized. Here, we show that central injection of MCH in mice deficient for kappa opoid receptor (k-OR) failed to stimulate feeding. To determine the hypothalamic area responsible for this MCH/k-OR interaction, we performed virogenetic studies and found that downregulation of k-OR by adeno-associated viruses (shOprk1-AAV) in LHA, but not in other hypothalamic nuclei, was sufficient to block MCH-induced food intake. Next, we sought to investigate the molecular signaling pathway within the LHA that mediates acute central MCH stimulation of food intake. We found that MCH activates k-OR and that increased levels of phosphorylated extracellular signal regulated kinase (ERK) are associated with downregulation of phospho-S6 Ribosomal Protein. This effect was prevented when a pharmacological inhibitor of k-OR was co-administered with MCH. Finally, the specific activation of the direct upstream regulator of S6 (p70S6K) in the LHA attenuated MCH-stimulated food consumption. Our results reveal that lateral hypothalamic k-OR system modulates the orexigenic action of MCH via the p70S6K/S6 pathway. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Interactions of orphanin FQ/nociceptin (OFQ/N) system with immune system factors and hypothalamic-pituitary-adrenal (HPA) axis.

    Science.gov (United States)

    Bodera, Paweł; Stankiewicz, Wanda; Kocik, Janusz

    2014-04-01

    Brain-immune system interactions and neurohormonal changes which are induced by psychophysiological factors are growing areas of scientific interest. Central (CNS) and autonomic nervous-endocrine-immune system pathways are connected with a number of behavioral and physiological factors which may be linked to disease susceptibility and progression. In this paper, influence of orphanin FQ/nociceptin receptor (OFQ/N) on the hypothalamic-pituitary-adrenal (HPA) axis and their influence on the immunological system was reviewed. The neuroendocrine system, in particular the hypothalamic-pituitary-adrenal (HPA) axis, is closely connected with the cytokines. HPA axis activation by cytokines, via the release of glucocorticoids has, in turn, been found to play a critical role in restraining and shaping immune responses. Investigation of the OFQ/N system and G-proteins suggests a role for this receptor as a down-regulator of cytokine, chemokine and chemokine receptor expression. Copyright © 2014 Institute of Pharmacology, Polish Academy of Sciences. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

  15. Impaired hypothalamic-pituitary-adrenocortical (HPA) system is related to severity of benzodiazepine withdrawal in patients with depression.

    Science.gov (United States)

    Wichniak, Adam; Brunner, Hans; Ising, Marcus; Pedrosa Gil, Francisco; Holsboer, Florian; Friess, Elisabeth

    2004-10-01

    A concatenation of data indicates that the pathogenesis of depression is related to an increased production and secretion of corticotropin-releasing hormone (CRH). Benzodiazepines profoundly suppress the basal and stress-related activation of the hypothalamic-pituitary-adrenocortical (HPA) system and discontinuation of these drugs results in rebound activation. We therefore investigated whether the extent of HPA system dysregulation is related to the severity of benzodiazepine withdrawal in patients with depression. We performed the combined dexamethasone/CRH test before benzodiazepine discontinuation (taper-off max. 5 mg diazepam-equivalents/week) in 14 depressed patients (13 f, 1 m, mean age 54.6 +/- 14.6) who responded to the antidepressant treatment. The severity of withdrawal symptoms was measured using the Clinical Institute Withdrawal Assessment-Benzodiazepines (CIWA-B) questionnaire. The depressive psychopathology was monitored using the Hamilton Depression Rating Scale, Montgomery Asberg Depression Rating Scale and Beck Depression Inventory. Patients with more severe benzodiazepine withdrawal (CIWA-B-increase > 14 pts; n = 7) showed a significant higher cortisol and ACTH response in the dexamethasone/CRH test preceding the discontinuation of benzodiazepines than patients displaying less severe withdrawal symptoms (CIWA-B-increase benzodiazepine withdrawal symptoms may be partly due to a disinhibition of the HPA system during discontinuation of benzodiazepines.

  16. Landmark discoveries in elucidating the origins of the hypothalamic-pituitary system from the perspective of a basal vertebrate, sea lamprey.

    Science.gov (United States)

    Sower, Stacia A

    2017-10-27

    The hypothalamic-pituitary (HP) system, which is specific to vertebrates, is considered to be an evolutionary innovation that emerged prior to or during the differentiation of the ancestral jawless vertebrates (agnathans) leading to the neuroendocrine control of many complex functions. Along with hagfish, lampreys represent the oldest lineage of vertebrates, agnathans (jawless fish). This review will highlight our discoveries of the major components of the lamprey HP axis. Generally, gnathostomes (jawed vertebrates) have one or two hypothalamic gonadotropin-releasing hormones (GnRH) while lampreys have three hypothalamic GnRHs. GnRH(s) regulate reproduction in all vertebrates via the pituitary. In gnathostomes, there are three classical pituitary glycoprotein hormones (luteinizing hormone, LH; follicle stimulating hormone, FSH; and thyrotropin, TSH) interacting specifically with three receptors, LH-R, FSH-R, and TSH-R, respectively. In general, FSH and LH regulate gonadal activity and TSH regulates thyroidal activity. In contrast to gnathostomes, we propose that lampreys only have two heterodimeric pituitary glycoprotein hormones, lamprey glycoprotein hormone (lGpH) and thyrostimulin, and two lamprey glycoprotein hormone receptors (lGpH-R I and -R II). Our existing data also suggest the existence of a primitive, overlapping yet functional hypothalamic-pituitary-gonadal (HPG) and HP-thyroidal (HPT) endocrine systems in lampreys. The study of basal vertebrates provides promising models for understanding the evolution of the hypothalamic-pituitary-thyroidal and gonadal axes in vertebrates. We hypothesize that the glycoprotein hormone/glycoprotein hormone receptor systems emerged as a link between the neuroendocrine and peripheral control levels during the early stages of gnathostome divergence. Our discovery of a functional HPG axis in lamprey has provided important clues for understanding the forces that ensured a common organization of the hypothalamus and pituitary

  17. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  18. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...

  19. Dysregulation of the sympathetic nervous system, hypothalamic-pituitary-adrenal axis and executive function in individuals at risk for suicide.

    Science.gov (United States)

    McGirr, Alexander; Diaconu, Gabriel; Berlim, Marcelo T; Pruessner, Jens C; Sablé, Rebecca; Cabot, Sophie; Turecki, Gustavo

    2010-11-01

    Suicidal behaviour aggregates in families, and the hypothalamic-pituitary-adrenal (HPA) axis and noradrenergic dysregulation may play a role in suicide risk. It is unclear whether stress dysregulation is a heritable trait of suicide or how it might increase risk. We investigated stress reactivity of the autonomic nervous system and the HPA axis in suicide predisposition and characterized the effect of this dysregulation on neuropsychologic function. In this family-based study of first-degree relatives (n = 14) of suicide completers and matched controls with no family or personal history of suicidal behaviour (n = 14), participants underwent the Trier Social Stress Test (TSST). We used salivary α-amylase and cortisol levels to characterize stress reactivity and diurnal variation. We administered a series of neuropsychologic and executive function tests before and after the TSST. Despite normal diurnal variation, relatives of suicide completers exhibited blunted cortisol and α-amylase TSST reactivity. Although there were no baseline differences in conceptual reasoning, sustained attention or executive function, the relatives of suicide completers did not improve on measures of inhibition upon repeated testing after TSST. Secondary analyses suggested that these effects were related to suicide vulnerability independent of major depression. The sample size was small, and the design prevents us from disentangling our findings from the possible traumatic consequences of losing a relative by suicide. Blunted stress response may be a trait of suicide risk, and impairment of stress-induced executive function may contribute to suicide vulnerability.

  20. Medial preoptic area interactions with dopamine neural systems in the control of the onset and maintenance of maternal behavior in rats.

    Science.gov (United States)

    Numan, Michael; Stolzenberg, Danielle S

    2009-01-01

    The medial preoptic area (MPOA) and dopamine (DA) neural systems interact to regulate maternal behavior in rats. Two DA systems are involved: the mesolimbic DA system and the incerto-hypothalamic DA system. The hormonally primed MPOA regulates the appetitive aspects of maternal behavior by activating mesolimbic DA input to the shell region of the nucleus accumbens (NAs). DA action on MPOA via the incerto-hypothalamic system may interact with steroid and peptide hormone effects so that MPOA output to the mesolimbic DA system is facilitated. Neural oxytocin facilitates the onset of maternal behavior by actions at critical nodes in this circuitry. DA-D1 receptor agonist action on either the MPOA or NAs can substitute for the effects of estradiol in stimulating the onset of maternal behavior, suggesting an overlap in underlying cellular mechanisms between estradiol and DA. Maternal memory involves the neural plasticity effects of mesolimbic DA activity. Finally, early life stressors may affect the development of MPOA-DA interactions and maternal behavior.

  1. Activation in the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system in women with carpal tunnel syndrome.

    Science.gov (United States)

    Fernández-de-Las-Peñas, César; Díaz-Rodríguez, Lourdes; Salom-Moreno, Jaime; Galiano-Castillo, Noelia; Valverde-Herreros, Lis; Martínez-Martín, Javier; Pareja, Juan A

    2014-08-01

    The aim of this study is to investigate the differences in salivary cortisol (hypothalamic-pituitary-adrenocortical [HPA] axis), α-amylase activity (sympathetic nervous system [SNS]), and immunoglobulin A (IgA; immune system) concentrations between women with carpal tunnel syndrome (CTS) and healthy women. A cross-sectional study. Activation of HPA, SNS, and immune system in CTS has not been clearly determined. One hundred two women (age: 45 ± 7 years) with electrodiagnostic and clinical diagnosis of CTS and 102 matched healthy women. The intensity of the pain was assessed with a Numerical Pain Rating Scale (0-10), and disability was determined with Boston Carpal Tunnel Questionnaire. Salivary cortisol concentration, α-amylase activity, salivary flow rate, and IgA concentration were collected from nonstimulated saliva. Women with CTS exhibited lower salivary flow rate (P  0.2) were found between groups as a total. Women with severe CTS exhibited lower salivary flow rate (P < 0.001), higher α-amylase activity (P = 0.002), and higher cortisol concentration (P = 0.03) than healthy women and than those with minimal/moderate CTS (P < 0.05). Within women with CTS, significant positive associations between α-amylase activity and the intensity of pain were found: the highest the level of pain, the higher the α-amylase activity, i.e., higher SNS activation. These results suggest that women with severe CTS exhibit changes in activation in the HPA axis and SNS but not in the humoral immune system. Activation of the SNS was associated with the intensity of pain. Future studies are needed to elucidate the direction of this relationship. Wiley Periodicals, Inc.

  2. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  3. Neural network system for traffic flow management

    Science.gov (United States)

    Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard

    1992-09-01

    Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.

  4. 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....

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

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

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

  6. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  7. Biological indicators of illness risk in offspring of bipolar parents: targeting the hypothalamic-pituitary-adrenal axis and immune system.

    Science.gov (United States)

    Duffy, Anne; Lewitzka, Ute; Doucette, Sarah; Andreazza, Ana; Grof, Paul

    2012-05-01

    The study aims to provide a selective review of the literature pertaining to the hypothalamic-pituitary-adrenal (HPA) axis and immune abnormalities as informative biological indicators of vulnerability in bipolar disorder (BD). We summarize key findings relating to HPA axis and immunological abnormalities in bipolar patients and their high-risk offspring. Findings derive from a review of selected original papers published in the literature, and supplemented by papers identified through bibliography review. Neurobiological findings are discussed in the context of emergent BD in those at genetic risk and synthesized into a neurodevelopmental model of illness onset and progression. BD is associated with a number of genetic and possibly epigenetic abnormalities associated with neurotransmitter, hormonal and immunologically mediated neurobiological pathways. Data from clinical and high-risk studies implicate HPA axis and immune system abnormalities, which may represent inherited vulnerabilities important for the transition to illness onset. Post-mortem and clinical studies implicate intracellular signal transduction processes and disturbance in energy metabolism associated with established BD. Specifically, long-standing maladaptive alterations such as changes in neuronal systems may be mediated through changes in intracellular signalling pathways, oxidative stress, cellular energy metabolism and apoptosis associated with substantial burden of illness. Prospective longitudinal studies of endophenotypes and biomarkers such as HPA axis and immune abnormalities in high-risk offspring will be helpful to understand genetically mediated biological pathways associated with illness onset and progression. A clinical staging model describing emergent illness in those at genetic risk should facilitate this line of investigation. © 2011 Blackwell Publishing Asia Pty Ltd.

  8. Chemical neuroimmunology: health in a nutshell bidirectional communication between immune and stress (limbic-hypothalamic-pituitary-adrenal) systems.

    Science.gov (United States)

    Lozovaya, Natalya; Miller, Andrew D

    2003-06-06

    Stress is a ubiquitous and pervasive part of modern life that is frequently blamed for causing a plethora of diseases and other discomforting medical conditions. All higher organisms, including humans, experience stress in the form of a wide variety of stressors that range from environmental pollutants and drugs to traumatic events or self-induced trauma. Stressors registered by the central nervous system (CNS) generate physiological stress responses in the body (periphery) by means of the limbic-hypothalamic-pituitary-adrenal (LHPA) axis. This LHPA axis operates through the use of chemical messengers such as the stress hormones corticotropin-releasing hormone (CRH) and glucocorticoids (GCs). Under conditions of frequent exposure to acute stress and/or chronic, long-term exposure to stress, the LHPA axis becomes dysfunctional and in the process frequently overproduces both CRH and GCs, which results in many mild to severely toxic side effects. Bidirectional communication between the LHPA axis and immune/inflammatory systems can dramatically potentiate these side effects and create environments in the CNS and periphery ripe for the triggering and/or promotion of tissue degeneration and disease. This review aims to present as far as possible a molecular view of the processes involved so as to provide a bridge from the diffuse range of studies on molecular structure and receptor interactions to the burgeoning biological and medical literature that describes the empirical interplay between stress and disease. We hope that our review of this fast-growing field, which we christen chemical neuroimmunology, will give a clear indication of the striking range and depth of current molecular, cellular and medical evidence linking stress hormones to degeneration and disease. In so doing, we hope to provide encouragement for others to become interested in this critical and far-reaching field of research, which is very much at the heart of many important disease processes and

  9. Analog neural network-based helicopter gearbox health monitoring system.

    Science.gov (United States)

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  10. Hypothalamic signaling mechanisms in hypertension.

    Science.gov (United States)

    Carmichael, Casey Y; Wainford, Richard D

    2015-05-01

    The etiology of hypertension, a critical public health issue affecting one in three US adults, involves the integration of the actions of multiple organ systems, including the central nervous system. Increased activation of the central nervous system, driving enhanced sympathetic outflow and increased blood pressure, has emerged as a major contributor to the pathogenesis of hypertension. The hypothalamus is a key brain site acting to integrate central and peripheral inputs to ultimately impact blood pressure in multiple disease states that evoke hypertension. This review highlights recent advances that have identified novel signal transduction mechanisms within multiple hypothalamic nuclei (e.g., paraventricular nucleus, arcuate nucleus) acting to drive the pathophysiology of hypertension in neurogenic hypertension, angiotensin II hypertension, salt-sensitive hypertension, chronic intermittent hypoxia, and obesity-induced hypertension. Increased understanding of hypothalamic activity in hypertension has the potential to identify novel targets for future therapeutic interventions designed to treat hypertension.

  11. 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

  12. 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.

  13. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  14. Hypothalamic survival circuits: blueprints for purposive behaviors.

    Science.gov (United States)

    Sternson, Scott M

    2013-03-06

    Neural processes that direct an animal's actions toward environmental goals are critical elements for understanding behavior. The hypothalamus is closely associated with motivated behaviors required for survival and reproduction. Intense feeding, drinking, aggressive, and sexual behaviors can be produced by a simple neuronal stimulus applied to discrete hypothalamic regions. What can these "evoked behaviors" teach us about the neural processes that determine behavioral intent and intensity? Small populations of neurons sufficient to evoke a complex motivated behavior may be used as entry points to identify circuits that energize and direct behavior to specific goals. Here, I review recent applications of molecular genetic, optogenetic, and pharmacogenetic approaches that overcome previous limitations for analyzing anatomically complex hypothalamic circuits and their interactions with the rest of the brain. These new tools have the potential to bridge the gaps between neurobiological and psychological thinking about the mechanisms of complex motivated behavior. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. 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.

  16. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

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

  17. Optical production systems using neural networks and symbolic substitution

    Science.gov (United States)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

    Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

  18. 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.

  19. Multiple neural network approaches to clinical expert systems

    Science.gov (United States)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  20. [Hypothalamic dysfunction in obesity].

    Science.gov (United States)

    van de Sande-Lee, Simone; Velloso, Licio A

    2012-08-01

    Obesity, defined as abnormal or excessive fat accumulation that may impair life quality, is one of the major public health problems worldwide. It results from an imbalance between food intake and energy expenditure. The control of energy balance in animals and humans is performed by the central nervous system (CNS) by means of neuroendocrine connections, in which circulating peripheral hormones, such as leptin and insulin, provide signals to specialized neurons of the hypothalamus reflecting body fat stores, and induce appropriate responses to maintain the stability of these stores. The majority of obesity cases are associated with central resistance to both leptin and insulin actions. In experimental animals, high-fat diets can induce an inflammatory process in the hypothalamus, which impairs leptin and insulin intracellular signaling pathways, and results in hyperphagia, decreased energy expenditure and, ultimately, obesity. Recent evidence obtained from neuroimaging studies and assessment of inflammatory markers in the cerebrospinal fluid of obese subjects suggests that similar alterations may be also present in humans. In this review, we briefly present the mechanisms involved with the loss of homeostatic control of energy balance in animal models of obesity, and the current evidence of hypothalamic dysfunction in obese humans.

  1. 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...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear 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...

  2. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  3. Lactation undernutrition leads to multigenerational molecular programming of hypothalamic gene networks controlling reproduction.

    Science.gov (United States)

    Kaczmarek, Monika M; Mendoza, Tamra; Kozak, Leslie P

    2016-05-04

    Reproductive success is dependent on development of hypothalamic circuits involving many hormonal systems working in concert to regulate gonadal function and sexual behavior. The timing of pubertal initiation and progression in mammals is likely influenced by the nutritional and metabolic state, leading us to the hypothesis that transient malnutrition experienced at critical times during development may perturb pubertal progression through successive generations. To test this hypothesis we have utilized a mouse model of undernutrition during suckling by exposing lactating mothers to undernutrition. Using a combination of transcriptomic and biological approaches, we demonstrate that molecular programming of hypothalamus may perturb gender specific phenotypes across generations that are dependent on the nutritional environment of the lactation period. Lactation undernutrition in first (F1) generation offspring affected body composition, reproductive performance parameters and influenced the expression of genes responsible for hypothalamic neural circuits controlling reproductive function of both sexes. Strikingly, F2 offspring showed phenotypes similar to F1 progeny; however, they were sex and parental nutritional history specific. Here, we showed that deregulated expression of genes involved in kisspeptin signaling within the hypothalamus is strongly associated with a delay in the attainment of puberty in F1 and F2 male and female offspring. The early developmental plasticity of hypothalamus when challenged with undernutrition during postnatal development not only leads to altered expression of genes controlling hypothalamic neural circuits, altered body composition, delayed puberty and disturbed reproductive performance in F1 progeny, but also affects F2 offspring, depending on parental malnutrition history and in sexually dimorphic manner.

  4. Anomalous hypothalamic responses to humor in cataplexy.

    Directory of Open Access Journals (Sweden)

    Allan L Reiss

    2008-05-01

    Full Text Available Cataplexy is observed in a subset of patients with narcolepsy and affects approximately 1 in 2,000 persons. Cataplexy is most often triggered by strong emotions such as laughter, which can result in transient, yet debilitating, muscle atonia. The objective of this study was to examine the neural systems underlying humor processing in individuals with cataplexy.While undergoing functional Magnetic Resonance Imaging (fMRI, we showed ten narcolepsy-cataplexy patients and ten healthy controls humorous cartoons. In addition, we examined the brain activity of one subject while in a full-blown cataplectic attack. Behavioral results showed that participants with cataplexy rated significantly fewer humorous cartoons as funny compared to controls. Concurrent fMRI showed that patients, when compared to controls and in the absence of overt cataplexy symptoms, showed pronounced activity in the emotional network including the ventral striatum and hypothalamus while viewing humorous versus non-humorous cartoons. Increased activity was also observed in the right inferior frontal gyri--a core component of the inhibitory circuitry. In comparison, the one subject who experienced a cataplectic attack showed dramatic reductions in hypothalamic activity.These findings suggest an overdrive of the emotional circuitry and possible compensatory suppression by cortical inhibitory regions in cataplexy. Moreover, during cataplectic attacks, the hypothalamus is characterized by a marked decrease in activity similar to that observed during sleep. One possible explanation for these findings is an initial overdrive and compensatory shutdown of the hypothalamus resulting in full cataplectic symptoms.

  5. Hypothalamic innate immune reaction in obesity.

    Science.gov (United States)

    Kälin, Stefanie; Heppner, Frank L; Bechmann, Ingo; Prinz, Marco; Tschöp, Matthias H; Yi, Chun-Xia

    2015-06-01

    Findings from rodent and human studies show that the presence of inflammatory factors is positively correlated with obesity and the metabolic syndrome. Obesity-associated inflammatory responses take place not only in the periphery but also in the brain. The hypothalamus contains a range of resident glial cells including microglia, macrophages and astrocytes, which are embedded in highly heterogenic groups of neurons that control metabolic homeostasis. This complex neural-glia network can receive information directly from blood-borne factors, positioning it as a metabolic sensor. Following hypercaloric challenge, mediobasal hypothalamic microglia and astrocytes enter a reactive state, which persists during diet-induced obesity. In established mouse models of diet-induced obesity, the hypothalamic vasculature displays angiogenic alterations. Moreover, proopiomelanocortin neurons, which regulate food intake and energy expenditure, are impaired in the arcuate nucleus, where there is an increase in local inflammatory signals. The sum total of these events is a hypothalamic innate immune reactivity, which includes temporal and spatial changes to each cell population. Although the exact role of each participant of the neural-glial-vascular network is still under exploration, therapeutic targets for treating obesity should probably be linked to individual cell types and their specific signalling pathways to address each dysfunction with cell-selective compounds.

  6. [Hypothalamic inflammation and energy balance deregulations: focus on chemokines.

    Science.gov (United States)

    Le Thuc, Ophélia; Rovère, Carole

    2016-01-01

    The hypothalamus is a key brain region in the regulation of energy balance. It especially controls food intake and both energy storage and expenditure through integration of humoral, neural and nutrient-related signals and cues. Hypothalamic neurons and glial cells act jointly to orchestrate, both spatially and temporally, regulated metabolic functions of the hypothalamus. Thus, the existence of a causal link between hypothalamic inflammation and deregulations of feeding behavior, such as involuntary weight-loss or obesity, has been suggested. Among the inflammatory mediators that could induce deregulations of hypothalamic control of the energy balance, chemokines represent interesting candidates. Indeed, chemokines, primarily known for their chemoattractant role of immune cells to the inflamed site, have also been suggested capable of neuromodulation. Thus, chemokines could disrupt cellular activity together with synthesis and/or secretion of multiple neurotransmitters/mediators that are involved in the maintenance of energy balance. Here, we relate, on one hand, recent results showing the primary role of the central chemokinergic signaling CCL2/CCR2 for metabolic and behavioral adaptation to high-grade inflammation, especially loss of appetite and weight, through its activity on hypothalamic neurons producing the orexigenic peptide Melanin-Concentrating Hormone (MCH) and, on the other hand, results that suggest that chemokines could also deregulate hypothalamic neuropeptidergic circuits to induce an opposite phenotype and eventually participate in the onset/development of obesity. In more details, we will emphasize a study recently showing, in a model of high-grade acute inflammation of LPS injection in mice, that central CCL2/CCR2 signaling is of primary importance for several aspects explaining weight loss associated with inflammation: after LPS injection, animals lose weight, reduce their food intake, increase their fat oxidation (thus energy consumption from

  7. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    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 applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  8. Neural signal sampling via the low power wireless pico system.

    Science.gov (United States)

    Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.

  9. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...

  10. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). 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...

  11. Neural expert decision support system for stroke diagnosis

    Science.gov (United States)

    Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana

    2017-08-01

    In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.

  12. The Role of Hypothalamic Neuropeptides in Neurogenesis and Neuritogenesis

    Science.gov (United States)

    Bakos, Jan; Zatkova, Martina; Bacova, Zuzana; Ostatnikova, Daniela

    2016-01-01

    The hypothalamus is a source of neural progenitor cells which give rise to different populations of specialized and differentiated cells during brain development. Newly formed neurons in the hypothalamus can synthesize and release various neuropeptides. Although term neuropeptide recently undergoes redefinition, small-size hypothalamic neuropeptides remain major signaling molecules mediating short- and long-term effects on brain development. They represent important factors in neurite growth and formation of neural circuits. There is evidence suggesting that the newly generated hypothalamic neurons may be involved in regulation of metabolism, energy balance, body weight, and social behavior as well. Here we review recent data on the role of hypothalamic neuropeptides in adult neurogenesis and neuritogenesis with special emphasis on the development of food intake and social behavior related brain circuits. PMID:26881105

  13. Reject mechanisms for massively parallel neural network character recognition systems

    Science.gov (United States)

    Garris, Michael D.; Wilson, Charles L.

    1992-12-01

    Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

  14. Neural-network-based fuzzy logic decision systems

    Science.gov (United States)

    Kulkarni, Arun D.; Giridhar, G. B.; Coca, Praveen

    1994-10-01

    During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.

  15. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

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

  16. Neuropeptide Y inhibits hypocretin/orexin neurons by multiple presynaptic and postsynaptic mechanisms: tonic depression of the hypothalamic arousal system.

    Science.gov (United States)

    Fu, Li-Ying; Acuna-Goycolea, Claudio; van den Pol, Anthony N

    2004-10-06

    Neurons that release neuropeptide Y (NPY) have important effects on hypothalamic homeostatic regulation, including energy homeostasis, and innervate hypocretin neurons. Using whole-cell patch-clamp recording, we explored NPY actions on hypocretin cells identified by selective green fluorescent protein expression in mouse hypothalamic slices. NPY reduced spike frequency and hyperpolarized the membrane potential of hypocretin neurons. The NPY hyperpolarizing action persisted in tetrodotoxin (TTX), was mimicked by Y1 receptor-selective agonists [Pro34]-NPY and [D-Arg25]-NPY, and was abolished by the Y1-specific antagonist BIBP3226 [(R)-N2-(diphenylacetyl)-N-[(4-hydroxyphenyl)methyl]-D-arginine-amide], consistent with a direct activation of postsynaptic Y1 receptors. NPY induced a current that was dependent on extracellular potassium, reversed near the potassium equilibrium potential, showed inward rectification, was blocked by extracellular barium, and was abolished by GDP-betaS in the recording pipette, consistent with a G-protein-activated inwardly rectifying K+ (GIRK) current. [Pro34]-NPY evoked, and BIBP3226 blocked, the activation of the GIRK-type current, indicating mediation by a Y1 receptor. NPY attenuated voltage-dependent calcium currents mainly via a Y1 receptor subtype. BIBP3226 increased spontaneous spike frequency, suggesting an ongoing Y1 receptor-mediated NPY inhibition. In TTX, miniature EPSCs were reduced in frequency but not amplitude by NPY, NPY13-36, and [D-Trp32]-NPY, but not by [Pro34]-NPY, suggesting the presynaptic inhibition was mediated by a Y2/Y5 receptor. NPY had little effect on GABA-mediated miniature IPSCs but depressed spontaneous IPSCs. Together, these data support the view that NPY reduces the activity of hypocretin neurons by multiple presynaptic and postsynaptic mechanisms and suggest NPY axons innervating hypocretin neurons may tonically attenuate hypocretin-regulated arousal.

  17. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    Science.gov (United States)

    Perera. J. Sebastian

    2000-01-01

    Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

  18. Space-time system architecture for the neural optical computing

    Science.gov (United States)

    Lo, Yee-Man V.

    1991-02-01

    The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.

  19. A dynamical systems view of motor preparation: Implications for neural prosthetic system design

    Science.gov (United States)

    Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.

    2013-01-01

    Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517

  20. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  1. Alterations in the hypothalamic melanocortin pathway in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Vercruysse, Pauline; Sinniger, Jérôme; El Oussini, Hajer; Scekic-Zahirovic, Jelena; Dieterlé, Stéphane; Dengler, Reinhard; Meyer, Thomas; Zierz, Stephan; Kassubek, Jan; Fischer, Wilhelm; Dreyhaupt, Jens; Grehl, Torsten; Hermann, Andreas; Grosskreutz, Julian; Witting, Anke; Van Den Bosch, Ludo; Spreux-Varoquaux, Odile; Ludolph, Albert C; Dupuis, Luc

    2016-04-01

    Amyotrophic lateral sclerosis, the most common adult-onset motor neuron disease, leads to death within 3 to 5 years after onset. Beyond progressive motor impairment, patients with amyotrophic lateral sclerosis suffer from major defects in energy metabolism, such as weight loss, which are well correlated with survival. Indeed, nutritional intervention targeting weight loss might improve survival of patients. However, the neural mechanisms underlying metabolic impairment in patients with amyotrophic lateral sclerosis remain elusive, in particular due to the lack of longitudinal studies. Here we took advantage of samples collected during the clinical trial of pioglitazone (GERP-ALS), and characterized longitudinally energy metabolism of patients with amyotrophic lateral sclerosis in response to pioglitazone, a drug with well-characterized metabolic effects. As expected, pioglitazone decreased glycaemia, decreased liver enzymes and increased circulating adiponectin in patients with amyotrophic lateral sclerosis, showing its efficacy in the periphery. However, pioglitazone did not increase body weight of patients with amyotrophic lateral sclerosis independently of bulbar involvement. As pioglitazone increases body weight through a direct inhibition of the hypothalamic melanocortin system, we studied hypothalamic neurons producing proopiomelanocortin (POMC) and the endogenous melanocortin inhibitor agouti-related peptide (AGRP), in mice expressing amyotrophic lateral sclerosis-linked mutant SOD1(G86R). We observed lower Pomc but higher Agrp mRNA levels in the hypothalamus of presymptomatic SOD1(G86R) mice. Consistently, numbers of POMC-positive neurons were decreased, whereas AGRP fibre density was elevated in the hypothalamic arcuate nucleus of SOD1(G86R) mice. Consistent with a defect in the hypothalamic melanocortin system, food intake after short term fasting was increased in SOD1(G86R) mice. Importantly, these findings were replicated in two other amyotrophic

  2. Functional MRI of human hypothalamic responses following glucose ingestion

    NARCIS (Netherlands)

    Smeets, P.A.M.; Graaf, C. de; Stafleu, A.; Osch, M.J.P. van; Grond, J. van der

    2005-01-01

    The hypothalamus is intimately involved in the regulation of food intake, integrating multiple neural and hormonal signals. Several hypothalamic nuclei contain glucose-sensitive neurons, which play a crucial role in energy homeostasis. Although a few functional magnetic resonance imaging (fMRI)

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

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

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

  4. Delayed puberty in spontaneously hypertensive rats involves a primary ovarian failure independent of the hypothalamic KiSS-1/GPR54/GnRH system.

    Science.gov (United States)

    Pinilla, L; Castellano, J M; Romero, M; Tena-Sempere, M; Gaytán, F; Aguilar, E

    2009-06-01

    Spontaneously hypertensive (SH) rats, extensively used as experimental models of essential human hypertension, display important alterations in the neuroendocrine reproductive axis, which manifest as markedly delayed puberty onset in females but whose basis remains largely unknown. We analyze herein in female SH rats: 1) possible alterations in the expression and function of KiSS-1/GPR54 and GnRH/GnRH-receptor systems, 2) the integrity of feedback mechanisms governing the hypothalamic-pituitary-ovarian axis, and 3) the control of ovarian function by gonadotropins. Our data demonstrate that, despite overtly delayed puberty, no significant decrease in hypothalamic KiSS-1, GPR54, or GnRH mRNA levels was detected in this strain. Likewise, in vivo gonadotropin responses to ovariectomy and systemic kisspeptin-10 or GnRH administration, as well as in vitro gonadotropin responses to GnRH, were fully preserved in SH rats. Moreover, circulating LH levels were grossly conserved during prepubertal maturation, whereas FSH levels were even enhanced from d 20 postpartum onwards. In striking contrast, ovarian weight and hormone (progesterone and testosterone) responses to human chorionic gonadotropin (CG) in vitro were profoundly decreased in SH rats, with impaired follicular development and delayed ovulation at puberty. Such reduced hormonal responses to human CG could not be attributed to changes in LH/CG or FSH-receptor mRNA expression but might be linked to blunted P450scc, 3beta-hydroxy steroid dehydrogenase, and aromatase mRNA levels in ovaries from SH rats. In conclusion, our results indicate that the expression and function of KiSS-1/GPR54 and GnRH/GnRH-receptor systems is normal in SH rats, whereas ovarian development, steroidogenesis, and responsiveness to gonadotropins are strongly compromised.

  5. Bariatric surgery in hypothalamic obesity

    Directory of Open Access Journals (Sweden)

    Nathan eBingham

    2012-02-01

    Full Text Available Craniopharyngiomas (CP are epithelial neoplasms generally found in the area of the pituitary and hypothalamus. Despite benign histology, these tumors and/or their treatment often result in significant, debilitating disorders of endocrine, neurological, behavioral, and metabolic systems. Severe obesity is observed in a high percentage of patients with CP resulting in significant comorbidities and negatively impacting quality of life. Obesity occurs as a result of hypothalamic damage and disruption of normal homeostatic mechanisms regulating energy balance. Such pathological weight gain, termed hypothalamic obesity (HyOb, is often severe and refractory to therapy.Unfortunately, neither lifestyle intervention nor pharmacotherapy has proven truly effective in the treatment of CP-HyOb. Given the limited choices and poor results of these treatments, several groups have examined bariatric surgery as a treatment alternative for patients with CP-HyOb. While a large body of evidence exists supporting the use of bariatric surgery in the treatment of exogenous obesity and its comorbidities, its role in the treatment of HyOb has yet to be well defined. To date, the existing literature on bariatric surgery in CP-HyOb is largely limited to case reports and series with short term follow-up. Here we review the current reports on the use of bariatric surgery in the treatment of CP-HyOb. We also compare these results to those reported for other populations of HyOb, including Prader-Willi Syndrome and patients with melanocortin signaling defects. While initial reports of bariatric surgery in CP-HyOb are promising, their limited scope makes it difficult to draw any substantial conclusions as to the long term safety and efficacy of bariatric surgery in CP-HyOb. There continues to be a need for more robust, controlled, prospective trials with long term follow-up in order to better define the role of bariatric surgery in the treatment of all types of hypothalamic

  6. Understanding how discrete populations of hypothalamic neurons orchestrate complicated behavioral states

    Directory of Open Access Journals (Sweden)

    Allison eGraebner

    2015-08-01

    Full Text Available A major question in systems neuroscience is how a single population of neurons can interact with the rest of the brain to orchestrate complex behavioral states. The hypothalamus contains many such discrete neuronal populations that individually regulate arousal, feeding, and drinking. For example, hypothalamic neurons that express hypocretin (Hcrt neuropeptides can sense homeostatic and metabolic factors affecting wakefulness and orchestrate organismal arousal. Neurons that express agouti-related protein (AgRP can sense the metabolic needs of the body and orchestrate a state of hunger. The organum vasculosum of the lamina terminalis (OVLT can detect the hypertonicity of blood and orchestrate a state of thirst. Each hypothalamic population is sufficient to generate complicated behavioral states through the combined efforts of distinct efferent projections. The principal challenge to understanding these brain systems is therefore to determine the individual roles of each downstream projection for each behavioral state. In recent years, the development and application of temporally precise, genetically encoded tools have greatly improved our understanding of the structure and function of these neural systems. This review will survey recent advances in our understanding of how these individual hypothalamic populations can orchestrate complicated behavioral states due to the combined efforts of individual downstream projections.

  7. Mediation of oxidative stress in hypothalamic ghrelin-associated appetite control in rats treated with phenylpropanolamine.

    Science.gov (United States)

    Yu, C-H; Chu, S-C; Chen, P-N; Hsieh, Y-S; Kuo, D-Y

    2017-04-01

    Phenylpropanolamine (PPA)-induced appetite control is associated with oxidative stress in the hypothalamus. This study explored whether hypothalamic antioxidants participated in hypothalamic ghrelin system-associated appetite control in PPA-treated rats. Rats were given PPA daily for 4 days, and changes in food intake and the expression of neuropeptide Y (NPY), the cocaine- and amphetamine-regulated transcript (CART), superoxide dismutase, catalase, ghrelin, acyl ghrelin (AG), ghrelin O-acyltransferase (GOAT) and the ghrelin receptor (GHSR1a) were examined and compared. Results showed that both food intake and the expression of NPY and ghrelin/AG/GOAT/GHSR1a decreased in response to PPA treatment with maximum decrease on Day 2 of the treatment. In contrast, the expression of antioxidants and CART increased, with the maximum increase on Day 2, with the expression opposite to that of NPY and ghrelin. A cerebral infusion of either a GHSR1a antagonist or reactive oxygen species scavenger modulated feeding behavior and NPY, CART, antioxidants and ghrelin system expression, showing the involvement of ghrelin signaling and oxidative stress in regulating PPA-mediated appetite control. We suggest that hypothalamic ghrelin signaling system, with the help of antioxidants, may participate in NPY/CART-mediated appetite control in PPA-treated rats. © 2016 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  8. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

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

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

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

  10. Dynamic causal models of neural system dynamics: current state ...

    Indian Academy of Sciences (India)

    2006-09-28

    Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...

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

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...

  12. A breathing circuit alarm system based on neural networks.

    Science.gov (United States)

    Orr, J A; Westenskow, D R

    1994-03-01

    The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.

  13. KCNQ potassium channels in sensory system and neural circuits.

    Science.gov (United States)

    Wang, Jing-jing; Li, Yang

    2016-01-01

    M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K(+) channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies.

  14. Rax regulates hypothalamic tanycyte differentiation and barrier function in mice

    Science.gov (United States)

    Miranda-Angulo, Ana L.; Byerly, Mardi S.; Mesa, Janny; Wang, Hong; Blackshaw, Seth

    2013-01-01

    The wall of the ventral third ventricle is composed of two distinct cell populations: tanycytes and ependymal cells. Tanycytes regulate many aspects of hypothalamic physiology, but little is known about the transcriptional network that regulates their development and function. We observed that the retina and anterior neural fold homeobox transcription factor (Rax) is selectively expressed in hypothalamic tanycytes, and showed a complementary pattern of expression to markers of hypothalamic ependymal cells, such as Rarres2 (retinoic acid receptor responder). To determine whether Rax controls tanycyte differentiation and function, we generated Rax haploinsufficient mice and examined their cellular and molecular phenotype in adulthood. These mice appeared grossly normal, but careful examination revealed a thinning of the third ventricular wall and reduction of both tanycyte and ependymal markers. These experiments show that Rax is required for hypothalamic tanycyte and ependymal cell differentiation. Rax haploinsufficiency also resulted in the ectopic presence of ependymal cells in the α2 tanycytic zone, where few ependymal cells are normally found, suggesting that Rax is selectively required for α2 tanycyte differentiation. These changes in the ventricular wall were associated with reduced diffusion of Evans Blue tracer from the ventricle to the hypothalamic parenchyma, with no apparent repercussion on the gross anatomical or behavioral phenotype of these mice. In conclusion, we have provided evidence that Rax is required for the normal differentiation and patterning of hypothalamic tanycytes and ependymal cells, as well as for maintenance of the cerebrospinal fluid-hypothalamus barrier. PMID:23939786

  15. Maternal social stress during late pregnancy affects hypothalamic-pituitary-adrenal function and brain neurotransmitter systems in pig offspring.

    Science.gov (United States)

    Otten, W; Kanitz, E; Couret, D; Veissier, I; Prunier, A; Merlot, E

    2010-04-01

    Maternal stress in pregnant sows may induce long-lasting alterations in the behavior, physiology, and immunity of their offspring. The aim of the present study was to investigate the consequences of repeated social stress during late gestation on determinants of the hypothalamic-pituitary-adrenal axis and on hippocampal neurotransmitter profiles in pig offspring. All pregnant gilts were housed in pairs. Each Stress gilt was mixed with an unfamiliar gilt twice a week between days 77 and 105 of gestation (n=18). Control gilts were housed in stable pairs over the same period (n=18). Plasma cortisol and corticosteroid binding globulin (CBG) were measured in 1 male and 1 female per litter in a basal situation on postnatal days (PND) 4, 26, and 60 and in a stressful situation at PND 28 (2 d after weaning) and 62 (2 d after relocation to a new building). Prenatal stress had no effect on plasma cortisol, but it decreased CBG at PND 26. Brain and adrenals were collected from 1 female per litter after weaning or relocation at PND 28 and PND 62. Adrenals were additionally collected at PND 4. Glucocorticoid receptor binding in the hippocampus and hypothalamus was not affected by prenatal treatment. However, prenatal stress increased the expression of 11beta-hydroxysteroid dehydrogenase type 1 mRNA in the hippocampus after weaning (P<0.05) and after relocation (P=0.08). In addition, prenatally stressed piglets showed an increased 5-hydroxyindole-3-acetic acid to 5-hydroxytryptamine ratio in the hippocampus after weaning and increased hippocampal c-fos mRNA expression and noradrenaline concentration after relocation (P<0.05). Prenatal stress also increased the relative adrenal weight at PND 4 and the cell density in the cortex and the medulla at PND 28, whereas no difference was found for activities of catecholamine-synthesising enzymes in the medulla. Overall, our data indicate that repeated social stress during pregnancy has long-lasting consequences on hypothalamic

  16. 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.

  17. Internal models and neural computation in the vestibular system.

    Science.gov (United States)

    Green, Andrea M; Angelaki, Dora E

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.

  18. Hypothalamic control systems show differential gene expression during spontaneous daily torpor and fasting-induced torpor in the Djungarian hamster (Phodopus sungorus.

    Directory of Open Access Journals (Sweden)

    Ceyda Cubuk

    Full Text Available Djungarian hamsters are able to use spontaneous daily torpor (SDT during the winter season as well as fasting-induced torpor (FIT at any time of the year to cope with energetically challenging environmental conditions. Torpor is a state of severely reduced metabolism with a pronounced decrease in body temperature, which enables animals to decrease their individual energy requirements. Despite sharing common characteristics, such as reduced body mass before first torpor expression and depressed metabolism and body temperature during the torpid state, FIT and SDT differ in several physiological properties including torpor bout duration, minimal body temperature, fuel utilization and circadian organization. It remains unclear, whether SDT and FIT reflect the same phenomenon or two different physiological states. The hypothalamus has been suggested to play a key role in regulating energy balance and torpor. To uncover differences in molecular control mechanisms of torpor expression, we set out to investigate hypothalamic gene expression profiles of genes related to orexigenic (Agrp/Npy, circadian clock (Bmal1/Per1 and thyroid hormone (Dio2/Mct8 systems of animals undergoing SDT and FIT during different torpor stages. Orexigenic genes were mainly regulated during FIT and remained largely unaffected by SDT. Expression patterns of clock genes showed disturbed circadian clock rhythmicity in animals undergoing FIT, but not in animals undergoing SDT. During both, SDT and FIT, decreased Dio2 expression was detected, indicating reduced hypothalamic T3 availability in both types of torpor. Taken together, our results provide evidence that SDT and FIT also differ in certain central control mechanisms and support the observation that animals undergoing SDT are in energetical balance, whereas animals undergoing FIT display a negative energy balance. This should be carefully taken into account when interpreting data in torpor research, especially from animal

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

    Science.gov (United States)

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

    2016-08-01

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

  20. Specific expression of optically active reporter gene in arginine vasopressin-secreting neurosecretory cells in the hypothalamic-neurohypophyseal system.

    Science.gov (United States)

    Ueta, Y; Fujihara, H; Dayanithi, G; Kawata, M; Murphy, D

    2008-06-01

    The anti-diuretic hormone arginine vasopressin (AVP) is synthesised in the magnocellular neurosecretory cells (MNCs) in the paraventricular nucleus (PVN) and the supraoptic nucleus (SON) of the hypothalamus. AVP-containing MNCs that project their axon terminals to the posterior pituitary can be identified using immunohistochemical techniques with specific antibodies recognising AVP and neurophysin II, and by virtue of their electrophysiological properties. Recently, we generated transgenic rats expressing an AVP-enhanced green fluorescent protein (eGFP) fusion gene in AVP-containing MNCs. In this transgenic rat, eGFP mRNA was observed in the PVN and the SON, and eGFP fluorescence was seen in the PVN and the SON, and also in the posterior pituitary, indicating transport of transgene protein down MNC axons to storage in nerve terminals. The expression of the AVP-eGFP transgene and eGFP fluorescence in the PVN and the SON was markedly increased after dehydration and chronic salt-loading. On the other hand, AVP-containing parvocellular neurosecretory cells in the PVN that are involved in the activation of the hypothalamic-pituitary adrenal axis exhibit robust AVP-eGFP fluorescence after bilateral adrenalectomy and intraperitoneal administration of lipopolysaccharide. In the median eminence, the internal and external layer showed strong fluorescence for eGFP after osmotic stimuli and stressful conditions, respectively, again indicating appropriate transport of transgene traslation products. Brain slices and acutely-dissociated MNCs and axon terminals also exhibited strong fluorescence, as observed under fluorescence microscopy. The AVP-eGFP transgenic animals are thus unique and provide a useful tool to study AVP-secreting cells in vivo for electrophysiology, imaging analysis such as intracellular Ca(2+) imaging, organ culture and in vivo monitoring of dynamic change in AVP secretion.

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

    Science.gov (United States)

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

    2016-07-01

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

  2. Neural - fuzzy approach for system identification

    NARCIS (Netherlands)

    Tien, B.T.

    1997-01-01

    Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such

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

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569

  4. 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.

  5. 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.

  6. Hypothalamic inflammation and gliosis in obesity.

    Science.gov (United States)

    Dorfman, Mauricio D; Thaler, Joshua P

    2015-10-01

    Hypothalamic inflammation and gliosis are recently discovered mechanisms that may contribute to obesity pathogenesis. Current research in this area suggests that investigation of these central nervous system responses may provide opportunities to develop new weight loss treatments. In rodents, hypothalamic inflammation and gliosis occur rapidly with high-fat diet consumption prior to significant weight gain. In addition, sensitivity or resistance to diet-induced obesity in rodents generally correlates with the presence or absence of hypothalamic inflammation and reactive gliosis (brain response to injury). Moreover, functional interventions that increase or decrease inflammation in neurons and glia correspondingly alter diet-associated weight gain. However, some conflicting data have recently emerged that question the contribution of hypothalamic inflammation to obesity pathogenesis. Nevertheless, several studies have detected gliosis and disrupted connectivity in obese humans, highlighting the potential translational importance of this mechanism. There is growing evidence that obesity is associated with brain inflammation in humans, particularly in the hypothalamus where its presence may disrupt body weight control and glucose homeostasis. More work is needed to determine whether this response is common in human obesity and to what extent it can be manipulated for therapeutic benefit.

  7. EFFECT OF ANESTHETIZING THE REGION OF THE PARAVENTRICULAR HYPOTHALAMIC NUCLEI ON ENERGY-METABOLISM DURING EXERCISE IN THE RAT

    NARCIS (Netherlands)

    VANDIJK, G; VISSING, J; STEFFENS, AB; GALBO, H

    The ventromedial and posterior hypothalamic nuclei are known to influence glucoregulation during exercise. The extensive projections of the paraventricular hypothalamic nucleus (PVN) to the sympathetic nervous system suggest that the PVN also may be involved in glucoregulation during exercise. The

  8. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    Sylvester, Jared; Reggia, James

    2016-07-01

    There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem

  9. 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.

  10. Hypothalamic regulation of brown adipose tissue thermogenesis and energy homeostasis

    Directory of Open Access Journals (Sweden)

    Wei eZhang

    2015-08-01

    Full Text Available Obesity and diabetes are increasing at an alarming rate worldwide, but the strategies for the prevention and treatment of these disorders remain inadequate. Brown adipose tissue (BAT is important for cold protection by producing heat using lipids and glucose as metabolic fuels. This thermogenic action causes increased energy expenditure and significant lipid/glucose disposal. In addition, BAT in white adipose tissue (WAT or beige cells have been found and they also exhibit the thermogenic action similar to BAT. These data provide evidence indicating BAT/beige cells as a potential target for combating obesity and diabetes. Recent discoveries of active BAT and beige cells in adult humans have further highlighted this potential. Growing studies have also shown the importance of central nervous system in the control of BAT thermogenesis and WAT browning using animal models. This review is focused on central neural thermoregulation, particularly addressing our current understanding of the importance of hypothalamic neural signaling in the regulation of BAT/beige thermogenesis and energy homeostasis.

  11. Hypothalamic dopaminergic neurons in an animal model of seasonal affective disorder.

    Science.gov (United States)

    Deats, Sean P; Adidharma, Widya; Yan, Lily

    2015-08-18

    Light has profound effects on mood regulation as exemplified in seasonal affective disorder (SAD) and the therapeutic benefits of light therapy. However, the underlying neural pathways through which light regulates mood are not well understood. Our previous work has developed the diurnal grass rat, Arvicanthis niloticus, as an animal model of SAD. Following housing conditions of either 12:12 h dim light:dark (DLD) or 8:16 h short photoperiod (SP), which mimic the lower light intensity or short day-length of winter, respectively, grass rats exhibit an increase in depression-like behavior compared to those housed in a 12:12 h bright light:dark (BLD) condition. Furthermore, we have shown that the orexinergic system is involved in mediating the effects of light on mood and anxiety. To explore other potential neural substrates involved in the depressive phenotype, the present study examined hypothalamic dopaminergic (DA) and somatostatin (SST) neurons in the brains of grass rats housed in DLD, SP and BLD. Using immunostaining for tyrosine hydroxylase (TH) and SST, we found that the number of TH- and SST-ir cells in the hypothalamus was significantly lower in the DLD and SP groups compared to the BLD group. We also found that treating BLD animals with a selective orexin receptor 1 (OX1R) antagonist SB-334867 significantly reduced the number of hypothalamic TH-ir cells. The present study suggests that the hypothalamic DA neurons are sensitive to daytime light deficiency and are regulated by an orexinergic pathway. The results support the hypothesis that the orexinergic pathways mediate the effects of light on other neuronal systems that collectively contribute to light-dependent changes in the affective state. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  12. Growth, IGF system, and cortisol in children with intrauterine growth retardation: is catch-up growth affected by reprogramming of the hypothalamic-pituitary-adrenal axis?

    Science.gov (United States)

    Cianfarani, Stefano; Geremia, Caterina; Scott, Carolyn D; Germani, Daniela

    2002-01-01

    Intrauterine growth retardation (IUGR) is one of the major causes of short stature in childhood. Although postnatal catch-up growth occurs in the majority of IUGR children, approximately 20% of them remain permanently short. The mechanisms that allow catch-up growth or, on the contrary, prevent IUGR children from achieving a normal height are still unknown. Our aim was to investigate whether intrauterine reprogramming of hypothalamic-pituitary-adrenal axis may be involved in postnatal growth retardation of IUGR children through a modulation of the function of the IGF system. Anthropometry, IGF system assessment, cortisol measurement, and lipid profile evaluation were performed in 49 IUGR children. Children were subdivided into two groups according to their actual height corrected for midparental height: CG (catch-up growth) group, 19 children with corrected height >or=0 z-score; and NCG (noncatch-up growth) group, 30 subjects with corrected height weight (p cortisol (p cortisol levels resulted inversely to birth weigh (r = -0.34, p 3.4 mM (130 mg/dL). LDL cholesterol was inversely related to birth weight (r = -0.31, p cortisol levels and first trimester length gain (r = -0.54, p cortisol (r = -0.67, p stress: children with increased cortisol secretion may be at higher risk of growth failure. During the neonatal period cortisol might act by limiting IGFBP-3 proteolysis and, therefore, reducing IGF bioavailability.

  13. Hypothalamic transcriptional expression of the kisspeptin system and sex steroid receptors differs among polycystic ovary syndrome rat models with different endocrine phenotypes.

    Science.gov (United States)

    Marcondes, Rodrigo Rodrigues; Carvalho, Kátia Cândido; Giannocco, Gisele; Duarte, Daniele Coelho; Garcia, Natália; Soares-Junior, José Maria; da Silva, Ismael Dale Cotrim Guerreiro; Maliqueo, Manuel; Baracat, Edmund Chada; Maciel, Gustavo Arantes Rosa

    2017-08-01

    Polycystic ovary syndrome is a heterogeneous endocrine disorder that affects reproductive-age women. The mechanisms underlying the endocrine heterogeneity and neuroendocrinology of polycystic ovary syndrome are still unclear. In this study, we investigated the expression of the kisspeptin system and gonadotropin-releasing hormone pulse regulators in the hypothalamus as well as factors related to luteinizing hormone secretion in the pituitary of polycystic ovary syndrome rat models induced by testosterone or estradiol. A single injection of testosterone propionate (1.25 mg) (n=10) or estradiol benzoate (0.5 mg) (n=10) was administered to female rats at 2 days of age to induce experimental polycystic ovary syndrome. Controls were injected with a vehicle (n=10). Animals were euthanized at 90-94 days of age, and the hypothalamus and pituitary gland were used for gene expression analysis. Rats exposed to testosterone exhibited increased transcriptional expression of the androgen receptor and estrogen receptor-β and reduced expression of kisspeptin in the hypothalamus. However, rats exposed to estradiol did not show any significant changes in hormone levels relative to controls but exhibited hypothalamic downregulation of kisspeptin, tachykinin 3 and estrogen receptor-α genes and upregulation of the gene that encodes the kisspeptin receptor. Testosterone- and estradiol-exposed rats with different endocrine phenotypes showed differential transcriptional expression of members of the kisspeptin system and sex steroid receptors in the hypothalamus. These differences might account for the different endocrine phenotypes found in testosterone- and estradiol-induced polycystic ovary syndrome rats.

  14. An In Vitro System Comprising Immortalized Hypothalamic Neuronal Cells (GT1–7 Cells for Evaluation of the Neuroendocrine Effects of Essential Oils

    Directory of Open Access Journals (Sweden)

    Dai Mizuno

    2015-01-01

    Full Text Available Aromatherapy and plant-based essential oils are widely used as complementary and alternative therapies for symptoms including anxiety. Furthermore, it was reportedly effective for the care of several diseases such as Alzheimer’s disease and depressive illness. To investigate the pharmacological effects of essential oils, we developed an in vitro assay system using immortalized hypothalamic neuronal cells (GT1–7 cells. In this study, we evaluated the effects of essential oils on neuronal death induced by hydrogen peroxide (H2O2, aluminum, zinc, or the antagonist of estrogen receptor (tamoxifen. Among tests of various essential oils, we found that H2O2-induced neuronal death was attenuated by the essential oils of damask rose, eucalyptus, fennel, geranium, ginger, kabosu, mandarin, myrrh, and neroli. Damask rose oil had protective effects against aluminum-induced neurotoxicity, while geranium and rosemary oil showed protective activity against zinc-induced neurotoxicity. In contrast, geranium oil and ginger oil enhanced the neurotoxicity of tamoxifen. Our in vitro assay system could be useful for the neuropharmacological and endocrine pharmacological studies of essential oils.

  15. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  16. Expanding the definition of hypothalamic obesity.

    Science.gov (United States)

    Hochberg, I; Hochberg, Z

    2010-10-01

    Hypothalamic obesity (HyOb) was first defined as the significant polyphagia and weight gain that occurs after extensive suprasellar operations for excision of hypothalamic tumours. However, polyphagia and weight gain complicate other disorders related to the hypothalamus, including those that cause structural damage to the hypothalamus like tumours, trauma, radiotherapy; genetic disorders such as Prader-Willi syndrome; side effects of psychotropic drugs; and mutations in several genes involved in hypothalamic satiety signalling. Moreover, 'simple' obesity is associated with polymorphisms in several genes involved in hypothalamic weight-regulating pathways. Thus, understanding HyOb may enhance our understanding of 'simple' obesity. This review will claim that HyOb is a far wider phenomenon than hitherto understood by the narrow definition of post-surgical weight gain. It will emphasize the similarity in clinical characteristics and therapeutic approaches for HyOb, as well as its mechanisms. HyOb, regardless of its aetiology, is a result of impairment in hypothalamic regulatory centres of body weight and energy expenditure. The pathophysiology includes loss of sensitivity to afferent peripheral humoral signals, such as, leptin on the one hand and dysfunctional afferent signals, on the other hand. The most important afferent signals deranged are energy regulation by the sympathetic nervous system and regulation of insulin secretion. Dys-regulation of 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) activity and melatonin may also have a role in the development of HyOb. The complexity of the syndrome requires simultaneous targeting of several mechanisms that are deranged in the HyOb patient. We review the studies evaluating possible treatment strategies, including sympathomimetics, somatostatin analogues, triiodothyronine, sibutramine, and surgery. © 2010 The Authors. obesity reviews © 2010 International Association for the Study of Obesity.

  17. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  18. Metabolic Context Regulates Distinct Hypothalamic Transcriptional Responses to Antiaging Interventions

    Directory of Open Access Journals (Sweden)

    Alexis M. Stranahan

    2012-01-01

    Full Text Available The hypothalamus is an essential relay in the neural circuitry underlying energy metabolism that needs to continually adapt to changes in the energetic environment. The neuroendocrine control of food intake and energy expenditure is associated with, and likely dependent upon, hypothalamic plasticity. Severe disturbances in energy metabolism, such as those that occur in obesity, are therefore likely to be associated with disruption of hypothalamic transcriptomic plasticity. In this paper, we investigated the effects of two well-characterized antiaging interventions, caloric restriction and voluntary wheel running, in two distinct physiological paradigms, that is, diabetic (db/db and nondiabetic wild-type (C57/Bl/6 animals to investigate the contextual sensitivity of hypothalamic transcriptomic responses. We found that, both quantitatively and qualitatively, caloric restriction and physical exercise were associated with distinct transcriptional signatures that differed significantly between diabetic and non-diabetic mice. This suggests that challenges to metabolic homeostasis regulate distinct hypothalamic gene sets in diabetic and non-diabetic animals. A greater understanding of how genetic background contributes to hypothalamic response mechanisms could pave the way for the development of more nuanced therapeutics for the treatment of metabolic disorders that occur in diverse physiological backgrounds.

  19. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Management of optic chiasmatic/hypothalamic astrocytomas in children

    Energy Technology Data Exchange (ETDEWEB)

    Steinbok, P.; Hentschel, S.; Almqvist, P.; Cochrane, D.D. [Univ. of British Columbia, British Columbia' s Children' s Hospital, Div. of Pediatric Neurosurgery, Dept. of Surgery, Vancouver, British Columbia (Canada); Poskitt, K. [Univ. of British Columbia, British Columbia' s Children' s Hospital, Dept. of Radiology, Vancouver, British Columbia (Canada)

    2002-05-01

    The management of optic chiasmatic gliomas is controversial, partly related to failure to separate out those tumors involving the optic chiasm only (chiasmatic tumors) from those also involving the hypothalamus (chiasmatic/hypothalamic tumors). The purpose of this study was: (i) to analyze the outcomes of chiasmatic and chiasmatic/hypothalamic tumors separately; and (ii) to determine the appropriateness of recommending radical surgical resection for the chiasmatic/hypothalamic tumors. A retrospective chart review of all newly diagnosed tumors involving the optic chiasm from 1982-1996 at British Columbia's Children's Hospital was performed. There were 32 patients less than 16 years of age, 14 with chiasmatic and 18 with chiasmatic/hypothalamic astrocytomas, with an average duration of follow-up of 5.8 years and 6.3 years, respectively. Ten of the patients with chiasmatic tumors and none with chiasmatic/hypothalamic tumors had neurofibromatosis I. Thirteen of the 14 chiasmatic tumors were managed with observation only, and none had progression requiring active intervention. For the chiasmatic/hypothalamic tumors. eight patients had subtotal resections (>95% resection), six had partial resections (50-95%), three had limited resections (<50%), and one had no surgery. There were fewer complications associated with the limited resections, especially with respect to hypothalamic dysfunction. There was no correlation between the extent of resection (subtotal, partial, or limited) and the time to tumor progression (average 18 months). In conclusion, chiasmatic and chiasmatic/hypothalamic tumors are different entities, which should be separated out for the Purposes of any study. For the chiasmatic/hypothalamic tumors, there was more morbidity and no prolongation of time to progression when radical resections were compared to more limited resections. Therefore, if surgery is performed, it may be appropriate to do a surgical procedure that strives only to provide a

  1. [A telemetery system for neural signal acquiring and processing].

    Science.gov (United States)

    Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.

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

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  3. 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...

  4. Autonomic nervous system and hypothalamic-pituitary-adrenal axis response to experimentally induced cold pain in adolescent non-suicidal self-injury--study protocol.

    Science.gov (United States)

    Koenig, Julian; Rinnewitz, Lena; Warth, Marco; Kaess, Michael

    2015-07-07

    Adolescent non-suicidal self-injury (NSSI) is associated with altered sensitivity to experimentally induced pain. Adolescents engaging in NSSI report greater pain threshold and pain tolerance, as well as lower pain intensity and pain unpleasantness compared to healthy controls. The experience of pain is associated with reactivity of both the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenal (HPA) axis. However, previous research has not yet systematically addressed differences in the physiological response to experimentally induced pain comparing adolescents with NSSI and age- and sex-matched healthy controls. Adolescents with NSSI and healthy controls undergo repeated painful stimulation with the cold pressor task. ANS activity is continuously recorded throughout the procedure to assess changes in heart rate and heart rate variability. Blood pressure is monitored and saliva is collected prior to and after nociceptive stimulation to assess levels of saliva cortisol. The study will provide evidence whether lower pain sensitivity in adolescents with NSSI is associated with blunted physiological and endocrinological responses to experimentally induced pain compared to healthy controls. Extending on the existing evidence on altered pain sensitivity in NSSI, measured by self-reports and behavioural assessments, this is the first study to take a systematic approach in evaluating the physiological response to experimentally induced pain in adolescent NSSI. Deutsche Register Klinischer Studien, Study ID: DRKS00007807; Trial Registration Date: 13.02.2015.

  5. Hypothalamic integration of immune function and metabolism.

    Science.gov (United States)

    Guijarro, Ana; Laviano, Alessandro; Meguid, Michael M

    2006-01-01

    The immune and neuroendocrine systems are closely involved in the regulation of metabolism at peripheral and central hypothalamic levels. In both physiological (meals) and pathological (infections, traumas and tumors) conditions immune cells are activated responding with the release of cytokines and other immune mediators (afferent signals). In the hypothalamus (central integration), cytokines influence metabolism by acting on nucleus involved in feeding and homeostasis regulation leading to the acute phase response (efferent signals) aimed to maintain the body integrity. Peripheral administration of cytokines, inoculation of tumor and induction of infection alter, by means of cytokine action, the normal pattern of food intake affecting meal size and meal number suggesting that cytokines acted differentially on specific hypothalamic neurons. The effect of cytokines-related cancer anorexia is also exerted peripherally. Increase plasma concentrations of insulin and free tryptophan and decrease gastric emptying and d-xylose absorption. In addition, in obesity an increase in interleukin (IL)-1 and IL-6 occurs in mesenteric fat tissue, which together with an increase in corticosterone, is associated with hyperglycemia, dyslipidemias and insulin resistance of obesity-related metabolic syndrome. These changes in circulating nutrients and hormones are sensed by hypothalamic neurons that influence food intake and metabolism. In anorectic tumor-bearing rats, we detected upregulation of IL-1beta and IL-1 receptor mRNA levels in the hypothalamus, a negative correlation between IL-1 concentration in cerebro-spinal fluid and food intake and high levels of hypothalamic serotonin, and these differences disappeared after tumor removal. Moreover, there is an interaction between serotonin and IL-1 in the development of cancer anorexia as well as an increase in hypothalamic dopamine and serotonin production. Immunohistochemical studies have shown a decrease in neuropeptide Y (NPY) and

  6. 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.

  7. 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.

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

    Science.gov (United States)

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

    2017-12-01

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

  9. Neural system modeling and simulation using Hybrid Functional Petri Net.

    Science.gov (United States)

    Tang, Yin; Wang, Fei

    2012-02-01

    The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

  10. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

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

    Science.gov (United States)

    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

    2014-06-05

    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 present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

  12. Delineating the regulation of energy homeostasis using hypothalamic cell models.

    Science.gov (United States)

    Wellhauser, Leigh; Gojska, Nicole M; Belsham, Denise D

    2015-01-01

    Attesting to its intimate peripheral connections, hypothalamic neurons integrate nutritional and hormonal cues to effectively manage energy homeostasis according to the overall status of the system. Extensive progress in the identification of essential transcriptional and post-translational mechanisms regulating the controlled expression and actions of hypothalamic neuropeptides has been identified through the use of animal and cell models. This review will introduce the basic techniques of hypothalamic investigation both in vivo and in vitro and will briefly highlight the key advantages and challenges of their use. Further emphasis will be place on the use of immortalized models of hypothalamic neurons for in vitro study of feeding regulation, with a particular focus on cell lines proving themselves most fruitful in deciphering fundamental basics of NPY/AgRP, Proglucagon, and POMC neuropeptide function. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Hypothalamic lipid-laden astrocytes induce microglia migration and activation.

    Science.gov (United States)

    Kwon, Yoon-Hee; Kim, Jiye; Kim, Chu-Sook; Tu, Thai Hien; Kim, Min-Seon; Suk, Kyoungho; Kim, Dong Hee; Lee, Byung Ju; Choi, Hye-Seon; Park, Taesun; Choi, Myung-Sook; Goto, Tsuyoshi; Kawada, Teruo; Ha, Tae Youl; Yu, Rina

    2017-06-01

    Obesity-induced hypothalamic inflammation is closely associated with various metabolic complications and neurodegenerative disorders. Astrocytes, the most abundant glial cells in the central nervous system, play a crucial role in pathological hypothalamic inflammatory processes. Here, we demonstrate that hypothalamic astrocytes accumulate lipid droplets under saturated fatty acid-rich conditions, such as obese environment, and that the lipid-laden astrocytes increase astrogliosis markers and inflammatory cytokines (TNFα, IL-1β, IL-6, MCP-1) at the transcript and/or protein level. Medium conditioned by the lipid-laden astrocytes stimulate microglial chemotactic activity and upregulate transcripts of the microglia activation marker Iba-1 and inflammatory cytokines. These findings indicate that the lipid-laden astrocytes formed in free fatty acid-rich obese condition may participate in obesity-induced hypothalamic inflammation through promoting microglia migration and activation. © 2017 Federation of European Biochemical Societies.

  14. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

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

  15. Separate neural systems value immediate and delayed monetary rewards.

    Science.gov (United States)

    McClure, Samuel M; Laibson, David I; Loewenstein, George; Cohen, Jonathan D

    2004-10-15

    When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.

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

    Science.gov (United States)

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

    2014-12-01

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

  17. Hypothalamic Inflammation and Energy Balance Disruptions: Spotlight on Chemokines

    OpenAIRE

    Le Thuc, Ophélia; Stobbe, Katharina; Cansell, Céline; Nahon, Jean-Louis; Blondeau, Nicolas; Rovère, Carole

    2017-01-01

    The hypothalamus is a key brain region in the regulation of energy balance as it controls food intake and both energy storage and expenditure through integration of humoral, neural, and nutrient-related signals and cues. Many years of research have focused on the regulation of energy balance by hypothalamic neurons, but the most recent findings suggest that neurons and glial cells, such as microglia and astrocytes, in the hypothalamus actually orchestrate together several metabolic functions....

  18. 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

  19. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  20. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  1. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  2. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Low-cost wireless neural recording system and software.

    Science.gov (United States)

    Gregory, Jeffrey A; Borna, Amir; Roy, Sabyasachi; Wang, Xiaoqin; Lewandowski, Brian; Schmidt, Marc; Najafi, Khalil

    2009-01-01

    We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25microV(rms) in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.

  4. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

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

  5. A direct-to-drive neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Justin P Kinney

    2015-09-01

    Full Text Available Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the data acquisition process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.

  6. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  7. Sodium tungstate regulates food intake and body weight through activation of the hypothalamic leptin pathway.

    Science.gov (United States)

    Amigó-Correig, M; Barceló-Batllori, S; Piquer, S; Soty, M; Pujadas, G; Gasa, R; Bortolozzi, A; Carmona, M C; Gomis, R

    2011-03-01

    Sodium tungstate is an anti-obesity drug targeting peripheral tissues. In vivo, sodium tungstate reduces body weight gain and food intake through increasing energy expenditure and lipid oxidation, but it also modulates hypothalamic gene expression when orally administered, raising the possibility of a direct effect of sodium tungstate on the central nervous system. Sodium tungstate was administered intraperitoneally (ip) to Wistar rats, and its levels were measured in cerebrospinal fluid through mass spectrometry. Body weight gain and food intake were monitored for 24 h after its administration in the third ventricle. Hypothalamic protein was obtained and subjected to western blot. In vitro, hypothalamic N29/4 cells were treated with 100 µM sodium tungstate or 1 nM leptin, and protein and neural gene expression were analysed. Sodium tungstate crossed the blood-brain barrier, reaching a concentration of 1.31 ± 0.07 mg/l in cerebrospinal fluid 30 min after ip injection. When centrally administered, sodium tungstate decreased body weight gain and food intake and increased the phosphorylation state of the main kinases and proteins involved in leptin signalling. In vitro, sodium tungstate increased the phosphorylation of janus kinase-2 (JAK2) and extracellular signal-regulated kinase-1/2 (ERK1/2), but the activation of each kinase did not depend on each other. It regulated c-myc gene expression through the JAK2/STAT system and c-fos and AgRP (agouti-related peptide) gene expression through the ERK1/2 pathway simultaneously and independently. Sodium tungstate increased the activity of several kinases involved in the leptin signalling system in an independent way, making it a suitable and promising candidate as a leptin-mimetic compound in order to manage obesity. © 2011 Blackwell Publishing Ltd.

  8. Hypothalamic interaction with the mesolimbic DA system in the control of the maternal and sexual behaviors in rats.

    Science.gov (United States)

    Stolzenberg, Danielle S; Numan, Michael

    2011-01-01

    The medial preoptic area (MPOA) of the hypothalamus regulates maternal behavior, male sexual behavior, and female sexual behavior. Functional neuroanatomical evidence indicates that the appetitive aspects of maternal behavior are regulated through MPOA interactions with the mesolimbic dopamine (DA) system; a major focus of this review is to explore whether or not the MPOA participates in the appetitive aspects of sexual behavior via its interaction with the mesolimbic DA system. A second focus of this review is to examine the extent to which estradiol interactions with DA within this circuit regulate all three reproductive behaviors. One mechanism through which estradiol activates male sexual behavior is through the potentiation of DA activity in the MPOA. In the hypothalamus, estradiol has also been found to act in concert with DA, through the activation of similar intracellular signaling pathways, in order to stimulate female sexual behavior. Finally, recent evidence suggests that some effects of estradiol are mediated by direct action of estradiol on the mesolimbic DA system. Copyright © 2010 Elsevier Ltd. All rights reserved.

  9. Multiagent Intrusion Detection Based on Neural Network Detectors and Artificial Immune System

    OpenAIRE

    Vaitsekhovich, L.; Golovko, V; Rubanau, V.

    2009-01-01

    In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...

  10. Identification of the non-linear systems using internal recurrent neural networks

    Directory of Open Access Journals (Sweden)

    Bogdan CODRES

    2006-12-01

    Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.

  11. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

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

  12. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

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

  13. Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Vukmirović

    2012-04-01

    Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

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

    Science.gov (United States)

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

    2016-07-01

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

  15. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  16. High ambient temperature reverses hypothalamic MC4 receptor overexpression in an animal model of anorexia nervosa.

    Science.gov (United States)

    Gutiérrez, E; Churruca, I; Zárate, J; Carrera, O; Portillo, M P; Cerrato, M; Vázquez, R; Echevarría, E

    2009-04-01

    The potential involvement of the melanocortin system in the beneficial effects of heat application in rats submitted to activity-based anorexia (ABA), an analogous model of anorexia nervosa (AN), was studied. Once ABA rats had lost 20% of body weight, half of the animals were exposed to a high ambient temperature (HAT) of 32 degrees C, whereas the rest were maintained at 21 degrees C. Control sedentary rats yoked to ABA animals received the same treatment. ABA rats (21 degrees C) showed increased Melanocortin 4 (MC4) receptor and Agouti gene Related Peptide (AgRP) expression, and decreased pro-opiomelanocortin (POMC) mRNA levels (Real Time PCR), with respect to controls. Heat application increased weight gain and food intake, and reduced running rate in ABA rats, when compared with ABA rats at 21 degrees C. However, no changes in body weight and food intake were observed in sedentary rats exposed to heat. Moreover, heat application reduced MC4 receptor, AgRP and POMC expression in ABA rats, but no changes were observed in control rats. These results indicate that hypothalamic MC4 receptor overexpression could occur on the basis of the characteristic hyperactivity, weight loss, and self-starvation of ABA rats, and suggest the involvement of hypothalamic melanocortin neural circuits in behavioural changes shown by AN patients. Changes in AgRP and POMC expression could represent an adaptative response to equilibrate energy balance. Moreover, the fact that HAT reversed hypothalamic MC4 receptor overexpression in ABA rats indicates the involvement of brain melanocortin system in the reported beneficial effects of heat application in AN. A combination of MC4 receptor antagonists and heat application could improve the clinical management of AN.

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

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

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

  18. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  19. Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems

    NARCIS (Netherlands)

    Vuurpijl, L.G.

    1998-01-01

    In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to

  20. Prenatal exposure to ethanol stimulates hypothalamic CCR2 chemokine receptor system: Possible relation to increased density of orexigenic peptide neurons and ethanol drinking in adolescent offspring

    Science.gov (United States)

    Chang, G.-Q.; Karatayev, O.; Leibowitz, S. F.

    2015-01-01

    Clinical and animal studies indicate that maternal consumption of ethanol during pregnancy increases alcohol drinking in the offspring. Possible underlying mechanisms may involve orexigenic peptides, which are stimulated by prenatal ethanol exposure and themselves promote drinking. Building on evidence that ethanol stimulates neuroimmune factors such as the chemokine CCL2 that in adult rats is shown to colocalize with the orexigenic peptide, melanin-concentrating hormone (MCH) in the lateral hypothalamus (LH), the present study sought to investigate the possibility that CCL2 or its receptor CCR2 in LH are stimulated by prenatal ethanol exposure, perhaps specifically within MCH neurons. Our paradigm of intraoral administration of ethanol to pregnant rats, at low-to-moderate doses (1 or 3 g/kg/day) during peak hypothalamic neurogenesis, caused in adolescent male offspring two-fold increase in drinking of and preference for ethanol and reinstatement of ethanol drinking in a two-bottle choice paradigm under an intermittent access schedule. This effect of prenatal ethanol exposure was associated with an increased expression of MCH and density of MCH+ neurons in LH of preadolescent offspring. Whereas CCL2+ cells at this age were low in density and unaffected by ethanol, CCR2+ cells were dense in LH and increased by prenatal ethanol, with a large percentage (83–87%) identified as neurons and found to colocalize MCH. Prenatal ethanol also stimulated the genesis of CCR2+ and MCH+ neurons in the embryo, which co-labeled the proliferation marker, BrdU. Ethanol also increased the genesis and density of neurons that co-expressed CCR2 and MCH in LH, with triple-labeled CCR2+/MCH+/BrdU+ neurons that were absent in control rats accounting for 35% of newly generated neurons in ethanol-exposed rats. With both the chemokine and MCH systems believed to promote ethanol consumption, this greater density of CCR2+/MCH+ neurons in the LH of preadolescent rats suggests that these systems

  1. Hypothalamic stimulation and baroceptor reflex interaction on renal nerve activity.

    Science.gov (United States)

    Wilson, M. F.; Ninomiya, I.; Franz, G. N.; Judy, W. V.

    1971-01-01

    The basal level of mean renal nerve activity (MRNA-0) measured in anesthetized cats was found to be modified by the additive interaction of hypothalamic and baroceptor reflex influences. Data were collected with the four major baroceptor nerves either intact or cut, and with mean aortic pressure (MAP) either clamped with a reservoir or raised with l-epinephrine. With intact baroceptor nerves, MRNA stayed essentially constant at level MRNA-0 for MAP below an initial pressure P1, and fell approximately linearly to zero as MAP was raised to P2. Cutting the baroceptor nerves kept MRNA at MRNA-0 (assumed to represent basal central neural output) independent of MAP. The addition of hypothalamic stimulation produced nearly constant increments in MRNA for all pressure levels up to P2, with complete inhibition at some level above P2. The increments in MRNA depended on frequency and location of the stimulus. A piecewise linear model describes MRNA as a linear combination of hypothalamic, basal central neural, and baroceptor reflex activity.

  2. Analysis of the developing neural system using an in vitro model by Raman spectroscopy.

    Science.gov (United States)

    Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi

    2015-04-07

    We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.

  3. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  4. 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.

  5. Differentiation of hypothalamic-like neurons from human pluripotent stem cells.

    Science.gov (United States)

    Wang, Liheng; Meece, Kana; Williams, Damian J; Lo, Kinyui Alice; Zimmer, Matthew; Heinrich, Garrett; Martin Carli, Jayne; Leduc, Charles A; Sun, Lei; Zeltser, Lori M; Freeby, Matthew; Goland, Robin; Tsang, Stephen H; Wardlaw, Sharon L; Egli, Dieter; Leibel, Rudolph L

    2015-02-01

    The hypothalamus is the central regulator of systemic energy homeostasis, and its dysfunction can result in extreme body weight alterations. Insights into the complex cellular physiology of this region are critical to the understanding of obesity pathogenesis; however, human hypothalamic cells are largely inaccessible for direct study. Here, we developed a protocol for efficient generation of hypothalamic neurons from human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) obtained from patients with monogenetic forms of obesity. Combined early activation of sonic hedgehog signaling followed by timed NOTCH inhibition in human ESCs/iPSCs resulted in efficient conversion into hypothalamic NKX2.1+ precursors. Application of a NOTCH inhibitor and brain-derived neurotrophic factor (BDNF) further directed the cells into arcuate nucleus hypothalamic-like neurons that express hypothalamic neuron markers proopiomelanocortin (POMC), neuropeptide Y (NPY), agouti-related peptide (AGRP), somatostatin, and dopamine. These hypothalamic-like neurons accounted for over 90% of differentiated cells and exhibited transcriptional profiles defined by a hypothalamic-specific gene expression signature that lacked pituitary markers. Importantly, these cells displayed hypothalamic neuron characteristics, including production and secretion of neuropeptides and increased p-AKT and p-STAT3 in response to insulin and leptin. Our results suggest that these hypothalamic-like neurons have potential for further investigation of the neurophysiology of body weight regulation and evaluation of therapeutic targets for obesity.

  6. Differentiation of hypothalamic-like neurons from human pluripotent stem cells

    Science.gov (United States)

    Wang, Liheng; Meece, Kana; Williams, Damian J.; Lo, Kinyui Alice; Zimmer, Matthew; Heinrich, Garrett; Martin Carli, Jayne; Leduc, Charles A.; Sun, Lei; Zeltser, Lori M.; Freeby, Matthew; Goland, Robin; Tsang, Stephen H.; Wardlaw, Sharon L.; Egli, Dieter; Leibel, Rudolph L.

    2015-01-01

    The hypothalamus is the central regulator of systemic energy homeostasis, and its dysfunction can result in extreme body weight alterations. Insights into the complex cellular physiology of this region are critical to the understanding of obesity pathogenesis; however, human hypothalamic cells are largely inaccessible for direct study. Here, we developed a protocol for efficient generation of hypothalamic neurons from human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) obtained from patients with monogenetic forms of obesity. Combined early activation of sonic hedgehog signaling followed by timed NOTCH inhibition in human ESCs/iPSCs resulted in efficient conversion into hypothalamic NKX2.1+ precursors. Application of a NOTCH inhibitor and brain-derived neurotrophic factor (BDNF) further directed the cells into arcuate nucleus hypothalamic-like neurons that express hypothalamic neuron markers proopiomelanocortin (POMC), neuropeptide Y (NPY), agouti-related peptide (AGRP), somatostatin, and dopamine. These hypothalamic-like neurons accounted for over 90% of differentiated cells and exhibited transcriptional profiles defined by a hypothalamic-specific gene expression signature that lacked pituitary markers. Importantly, these cells displayed hypothalamic neuron characteristics, including production and secretion of neuropeptides and increased p-AKT and p-STAT3 in response to insulin and leptin. Our results suggest that these hypothalamic-like neurons have potential for further investigation of the neurophysiology of body weight regulation and evaluation of therapeutic targets for obesity. PMID:25555215

  7. Nicotinic α4 Receptor-Mediated Cholinergic Influences on Food Intake and Activity Patterns in Hypothalamic Circuits.

    Directory of Open Access Journals (Sweden)

    Ana P García

    Full Text Available Nicotinic acetylcholine receptors (nAChRs play an important role in regulating appetite and have been shown to do so by influencing neural activity in the hypothalamus. To shed light on the hypothalamic circuits governing acetylcholine's (ACh regulation of appetite this study investigated the influence of hypothalamic nAChRs expressing the α4 subunit. We found that antagonizing the α4β2 nAChR locally in the lateral hypothalamus with di-hydro-ß-erythroidine (DHβE, an α4 nAChR antagonist with moderate affinity, caused an increase in food intake following free access to food after a 12 hour fast, compared to saline-infused animals. Immunocytochemical analysis revealed that orexin/hypocretin (HO, oxytocin, and tyrosine hydroxylase (TH-containing neurons in the A13 and A12 of the hypothalamus expressed the nAChR α4 subunit in varying amounts (34%, 42%, 50%, and 51%, respectively whereas melanin concentrating hormone (MCH neurons did not, suggesting that DHβE-mediated increases in food intake may be due to a direct activation of specific hypothalamic circuits. Systemic DHβE (2 mg/kg administration similarly increased food intake following a 12 hour fast. In these animals a subpopulation of orexin/hypocretin neurons showed elevated activity compared to control animals and MCH neuronal activity was overall lower as measured by expression of the immediate early gene marker for neuronal activity cFos. However, oxytocin neurons in the paraventricular hypothalamus and TH-containing neurons in the A13 and A12 did not show differential activity patterns. These results indicate that various neurochemically distinct hypothalamic populations are under the influence of α4β2 nAChRs and that cholinergic inputs to the lateral hypothalamus can affect satiety signals through activation of local α4β2 nAChR-mediated transmission.

  8. Nicotinic α4 Receptor-Mediated Cholinergic Influences on Food Intake and Activity Patterns in Hypothalamic Circuits.

    Science.gov (United States)

    García, Ana P; Aitta-aho, Teemu; Schaaf, Laura; Heeley, Nicholas; Heuschmid, Lena; Bai, Yunjing; Barrantes, Francisco J; Apergis-Schoute, John

    2015-01-01

    Nicotinic acetylcholine receptors (nAChRs) play an important role in regulating appetite and have been shown to do so by influencing neural activity in the hypothalamus. To shed light on the hypothalamic circuits governing acetylcholine's (ACh) regulation of appetite this study investigated the influence of hypothalamic nAChRs expressing the α4 subunit. We found that antagonizing the α4β2 nAChR locally in the lateral hypothalamus with di-hydro-ß-erythroidine (DHβE), an α4 nAChR antagonist with moderate affinity, caused an increase in food intake following free access to food after a 12 hour fast, compared to saline-infused animals. Immunocytochemical analysis revealed that orexin/hypocretin (HO), oxytocin, and tyrosine hydroxylase (TH)-containing neurons in the A13 and A12 of the hypothalamus expressed the nAChR α4 subunit in varying amounts (34%, 42%, 50%, and 51%, respectively) whereas melanin concentrating hormone (MCH) neurons did not, suggesting that DHβE-mediated increases in food intake may be due to a direct activation of specific hypothalamic circuits. Systemic DHβE (2 mg/kg) administration similarly increased food intake following a 12 hour fast. In these animals a subpopulation of orexin/hypocretin neurons showed elevated activity compared to control animals and MCH neuronal activity was overall lower as measured by expression of the immediate early gene marker for neuronal activity cFos. However, oxytocin neurons in the paraventricular hypothalamus and TH-containing neurons in the A13 and A12 did not show differential activity patterns. These results indicate that various neurochemically distinct hypothalamic populations are under the influence of α4β2 nAChRs and that cholinergic inputs to the lateral hypothalamus can affect satiety signals through activation of local α4β2 nAChR-mediated transmission.

  9. Novel aspects of hypothalamic-pituitary-adrenal axis regulation and glucocorticoid actions

    Science.gov (United States)

    Uchoa, Ernane Torres; Aguilera, Greti; Herman, James P.; Fiedler, Jenny L.; Deak, Terrence; Cordeiro de Sousa, Maria Bernardete

    2014-01-01

    Normal hypothalamic-pituitary-adrenal (HPA) axis activity leading to rhythmic and episodic release of adrenal glucocorticoids is essential for body homeostasis and survival during stress. Acting through specific intracellular receptors in the brain and periphery, glucocorticoids regulate behavior, metabolic, cardiovascular, immune, and neuroendocrine activities. In contrast to chronic elevated levels, circadian and acute stress-induced increases in glucocorticoids are necessary for hippocampal neuronal survival and memory acquisition and consolidation, through inhibiting apoptosis, facilitating glutamate transmission and inducing immediate early genes and spine formation. In addition to its metabolic actions leading to increasing energy availability, glucocorticoids have profound effects on feeding behavior, mainly through modulation of orexigenic and anorixegenic neuropeptides. Evidence is also emerging that in addition to the recognized immune suppressive actions of glucocorticoids by counteracting adrenergic proinflammatory actions, circadian elevations have priming effects in the immune system, potentiating acute defensive responses. In addition, negative feedback by glucocorticoids involves multiple mechanisms leading to limiting HPA axis activation and preventing deleterious effects of excessive glucocorticoid production. Adequate glucocorticoid secretion to meet body demands is tightly regulated by a complex neural circuitry controlling hypothalamic corticotrophin releasing hormone (CRH) and vasopressin secretion, the main regulators of pituitary adrenocorticotrophic hormone (ACTH). Rapid feedback mechanisms, likely involving non-genomic actions of glucocorticoids, mediate immediate inhibition of hypothalamic CRH and ACTH secretion, while intermediate and delayed mechanisms mediated by genomic actions involve modulation of limbic circuitry and peripheral metabolic messengers. Consistent with their key adaptive roles, HPA axis components are evolutionarily

  10. 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......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...... 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...

  11. 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...

  12. 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.

  13. On the Computational Power of Spiking Neural P Systems with Self-Organization

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-06-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

  14. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  15. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  16. An alternative respiratory sounds classification system utilizing artificial neural networks.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

    2015-01-01

    Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  17. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  18. 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.

  19. Loss of the anorexic response to systemic 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside administration despite reducing hypothalamic AMP-activated protein kinase phosphorylation in insulin-deficient rats.

    Directory of Open Access Journals (Sweden)

    Kaio F Vitzel

    Full Text Available This study tested whether chronic systemic administration of 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR could attenuate hyperphagia, reduce lean and fat mass losses, and improve whole-body energy homeostasis in insulin-deficient rats. Male Wistar rats were first rendered diabetic through streptozotocin (STZ administration and then intraperitoneally injected with AICAR for 7 consecutive days. Food and water intake, ambulatory activity, and energy expenditure were assessed at the end of the AICAR-treatment period. Blood was collected for circulating leptin measurement and the hypothalami were extracted for the determination of suppressor of cytokine signaling 3 (SOCS3 content, as well as the content and phosphorylation of AMP-kinase (AMPK, acetyl-CoA carboxylase (ACC, and the signal transducer and activator of transcription 3 (STAT3. Rats were thoroughly dissected for adiposity and lean body mass (LBM determinations. In non-diabetic rats, despite reducing adiposity, AICAR increased (∼1.7-fold circulating leptin and reduced hypothalamic SOCS3 content and food intake by 67% and 25%, respectively. The anorexic effect of AICAR was lost in diabetic rats, even though hypothalamic AMPK and ACC phosphorylation markedly decreased in these animals. Importantly, hypothalamic SOCS3 and STAT3 levels remained elevated and reduced, respectively, after treatment of insulin-deficient rats with AICAR. Diabetic rats were lethargic and displayed marked losses of fat and LBM. AICAR treatment increased ambulatory activity and whole-body energy expenditure while also attenuating diabetes-induced fat and LBM losses. In conclusion, AICAR did not reverse hyperphagia, but it promoted anti-catabolic effects on skeletal muscle and fat, enhanced spontaneous physical activity, and improved the ability of rats to cope with the diabetes-induced dysfunctional alterations in glucose metabolism and whole-body energy homeostasis.

  20. Loss of the anorexic response to systemic 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside administration despite reducing hypothalamic AMP-activated protein kinase phosphorylation in insulin-deficient rats.

    Science.gov (United States)

    Vitzel, Kaio F; Bikopoulos, George; Hung, Steven; Curi, Rui; Ceddia, Rolando B

    2013-01-01

    This study tested whether chronic systemic administration of 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR) could attenuate hyperphagia, reduce lean and fat mass losses, and improve whole-body energy homeostasis in insulin-deficient rats. Male Wistar rats were first rendered diabetic through streptozotocin (STZ) administration and then intraperitoneally injected with AICAR for 7 consecutive days. Food and water intake, ambulatory activity, and energy expenditure were assessed at the end of the AICAR-treatment period. Blood was collected for circulating leptin measurement and the hypothalami were extracted for the determination of suppressor of cytokine signaling 3 (SOCS3) content, as well as the content and phosphorylation of AMP-kinase (AMPK), acetyl-CoA carboxylase (ACC), and the signal transducer and activator of transcription 3 (STAT3). Rats were thoroughly dissected for adiposity and lean body mass (LBM) determinations. In non-diabetic rats, despite reducing adiposity, AICAR increased (∼1.7-fold) circulating leptin and reduced hypothalamic SOCS3 content and food intake by 67% and 25%, respectively. The anorexic effect of AICAR was lost in diabetic rats, even though hypothalamic AMPK and ACC phosphorylation markedly decreased in these animals. Importantly, hypothalamic SOCS3 and STAT3 levels remained elevated and reduced, respectively, after treatment of insulin-deficient rats with AICAR. Diabetic rats were lethargic and displayed marked losses of fat and LBM. AICAR treatment increased ambulatory activity and whole-body energy expenditure while also attenuating diabetes-induced fat and LBM losses. In conclusion, AICAR did not reverse hyperphagia, but it promoted anti-catabolic effects on skeletal muscle and fat, enhanced spontaneous physical activity, and improved the ability of rats to cope with the diabetes-induced dysfunctional alterations in glucose metabolism and whole-body energy homeostasis.

  1. 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.

  2. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  3. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  4. Melatonin controls seasonal breeding by a network of hypothalamic targets

    DEFF Research Database (Denmark)

    Revel, Florent G; Masson-Pévet, Mireille; Pévet, Paul

    2009-01-01

    /GPR54 system and to the RFamide-related peptides.Interestingly, these systems involve different hypothalamic nuclei, suggesting that several brain loci may be crucial for melatonin to regulate reproduction, and thus represent key starting points to identify the long-sought-after mode and site...

  5. Fault diagnosis in satellite attitude control systems using artificial neural networkk

    Science.gov (United States)

    Ayodele I., Olanipekun

    The nonlinear behavior exhibited by altitude control system processes and also the presence of external constraints on the operating conditions causes hitch in the dynamics of system processes. This research work proposes a fault detection/tolerant prediction in an altitude control system. This is done through the artificial neural network fault detection by deploying the neural network approach. A fault detection and isolation module is developed in the actuator system of the Altitude Control System, thereby achieving the goal of this thesis. This can be done by two basic classification stages: Neural Residual Generator (Neural Observer)- This stage is responsible for generating residual errors that can reflect the real behavior of the entire process as against its normal conditions. Adaptive Neural Classifier - This stage is responsible for managing the isolation task of the fault detected by evaluating the generated residual errors from the neural estimator which gives detailed information about faults detected e.g., fault location and time. These two stages can be implemented by executing the tasks listed below: 1. Study and develop a generic three axis stabilized altitude control model based on the reaction wheels. This is established with three separate PD controllers designed for each reaction wheel of the satellite axis using the Matlab - SIMULINK. 2. Develop a dynamic neural network residual generator based on Dynamic Multilayer Perceptron Network (DMLP) which is then applied to the reaction wheel model designed commonly called the actuator in the altitude control system of a satellite 3. Develop a neural network adaptive classifier based on the Learning Vector Quantization (LVQ) model which is used for the isolation concept. The advantages of the proposed dynamic neural network and neural adaptive classifier approach are showcased.

  6. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  7. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Progress Toward Adaptive Integration and Optimization of Automated and Neural Processing Systems: Establishing Neural and Behavioral Benchmarks of Optimized Performance

    Science.gov (United States)

    2014-11-01

    grid, using an Advanced Brain Monitoring (ABM) ×24 system configured with the single-trial event - related potential (ERP) sensor strip and operating...ROC curve BCI brain-computer interface EEG electroencephalogram ERP event - related potential EVUS estimated volume under the surface FOV field of...stations. 15. SUBJECT TERMS rapid serial visual presentation, RSVP, EEG, neural classification, P300 , brain-computer interface 16. SECURITY

  9. The role of NPY in hypothalamic mediated food intake.

    Science.gov (United States)

    Mercer, Rebecca E; Chee, Melissa J S; Colmers, William F

    2011-10-01

    Neuropeptide Y (NPY) is a highly conserved neuropeptide with orexigenic actions in discrete hypothalamic nuclei that plays a role in regulating energy homeostasis. NPY signals via a family of high affinity receptors that mediate the widespread actions of NPY in all hypothalamic nuclei. These actions are also subject to tight, intricate regulation by numerous peripheral and central energy balance signals. The NPY system is embedded within a densely-redundant network designed to ensure stable energy homeostasis. This redundancy may underlie compensation for the loss of NPY or its receptors in germline knockouts, explaining why conventional knockouts of NPY or its receptors rarely yield a marked phenotypic change. We discuss insights into the hypothalamic role of NPY from studies of its physiological actions, responses to genetic manipulations and interactions with other energy balance signals. We conclude that numerous approaches must be employed to effectively study different aspects of NPY action. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. 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.......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...

  11. 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

  12. Artificial Neural Systems Application to the Simulation of Air Combat Decision Making

    Science.gov (United States)

    1992-04-01

    unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates

  13. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  14. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  15. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  16. 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)

  17. Hypothalamic obesity: causes, consequences, treatment.

    Science.gov (United States)

    Lustig, Robert H

    2008-12-01

    Hypothalamic obesity, or intractable weight gain after hypothalamic damage, is one of the most pernicious and agonizing late effects of CNS insult. Such patients gain weight even in response to caloric restriction, and attempts at lifestyle modification are useless to prevent or treat the obesity. The pathogenesis of this condition involves the inability to transduce afferent hormonal signals of adiposity, in effect mimicing a state of CNS starvation. Efferent sympathetic activity drops, resulting in malaise and reduced energy expenditure, and vagal activity increases, resulting in increased insulin secretion and adipogenesis. Pharmacologic treatment is difficult, consisting of adrenergics to mimick sympathetic activity, or suppression of insulin secretion with octreotide, or both. Recently, bariatric surgery (Roux-en-Y gastric bypass, laparoscopic gastric banding, vagotomy) have also been attempted with variable results. Early and intensive management is required to stave off the obesity and its consequences.

  18. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  19. 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...

  20. 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...

  1. Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

    OpenAIRE

    Liu, Da; Li, Muguo

    2015-01-01

    This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics ...

  2. 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.

  3. 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.

  4. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  5. NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems

    Directory of Open Access Journals (Sweden)

    Caglayan Ozan

    2017-10-01

    Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

  6. A genomic atlas of mouse hypothalamic development

    Science.gov (United States)

    Shimogori, Tomomi; Lee, Daniel A; Miranda-Angulo, Ana; Yang, Yanqin; Wang, Hong; Jiang, Lizhi; Yoshida, Aya C; Kataoka, Ayane; Mashiko, Hiromi; Avetisyan, Marina; Qi, Lixin; Qian, Jiang; Blackshaw, Seth

    2014-01-01

    The hypothalamus is a central regulator of many behaviors that are essential for survival, such as temperature regulation, food intake and circadian rhythms. However, the molecular pathways that mediate hypothalamic development are largely unknown. To identify genes expressed in developing mouse hypothalamus, we performed microarray analysis at 12 different developmental time points. We then conducted developmental in situ hybridization for 1,045 genes that were dynamically expressed over the course of hypothalamic neurogenesis. We identified markers that stably labeled each major hypothalamic nucleus over the entire course of neurogenesis and constructed a detailed molecular atlas of the developing hypothalamus. As a proof of concept of the utility of these data, we used these markers to analyze the phenotype of mice in which Sonic Hedgehog (Shh) was selectively deleted from hypothalamic neuroepithelium and found that Shh is essential for anterior hypothalamic patterning. Our results serve as a resource for functional investigations of hypothalamic development, connectivity, physiology and dysfunction. PMID:20436479

  7. 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...

  8. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

  9. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  10. Software implementation of artificial neural networks in automated intelligent systems

    Directory of Open Access Journals (Sweden)

    В.П. Харченко

    2009-02-01

    Full Text Available  Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.

  11. Credit Risk Evaluation System: An Artificial Neural Network Approach

    African Journals Online (AJOL)

    In this paper, we proposed an architecture which uses the theory of artificial neural networks and business rules to correctly determine whether a customer is good or bad. In the first step, by using clustering algorithm, clients are segmented into groups with similar features. In the second step, decision trees are built based ...

  12. Integrating resource defence theory with a neural nonapeptide pathway to explain territory-based mating systems.

    Science.gov (United States)

    Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A

    2015-01-01

    The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.

  13. Hypothalamic Inflammation and Energy Balance Disruptions: Spotlight on Chemokines

    Directory of Open Access Journals (Sweden)

    Ophélia Le Thuc

    2017-08-01

    Full Text Available The hypothalamus is a key brain region in the regulation of energy balance as it controls food intake and both energy storage and expenditure through integration of humoral, neural, and nutrient-related signals and cues. Many years of research have focused on the regulation of energy balance by hypothalamic neurons, but the most recent findings suggest that neurons and glial cells, such as microglia and astrocytes, in the hypothalamus actually orchestrate together several metabolic functions. Because glial cells have been described as mediators of inflammatory processes in the brain, the existence of a causal link between hypothalamic inflammation and the deregulations of feeding behavior, leading to involuntary weight loss or obesity for example, has been suggested. Several inflammatory pathways that could impair the hypothalamic control of energy balance have been studied over the years such as, among others, toll-like receptors and canonical cytokines. Yet, less studied so far, chemokines also represent interesting candidates that could link the aforementioned pathways and the activity of hypothalamic neurons. Indeed, chemokines, in addition to their role in attracting immune cells to the inflamed site, have been suggested to be capable of neuromodulation. Thus, they could disrupt cellular activity together with synthesis and/or secretion of multiple neurotransmitters/mediators involved in the maintenance of energy balance. This review discusses the different inflammatory pathways that have been identified so far in the hypothalamus in the context of feeding behavior and body weight control impairments, with a particular focus on chemokines signaling that opens a new avenue in the understanding of the major role played by inflammation in obesity.

  14. Hypothalamic Inflammation and Energy Balance Disruptions: Spotlight on Chemokines.

    Science.gov (United States)

    Le Thuc, Ophélia; Stobbe, Katharina; Cansell, Céline; Nahon, Jean-Louis; Blondeau, Nicolas; Rovère, Carole

    2017-01-01

    The hypothalamus is a key brain region in the regulation of energy balance as it controls food intake and both energy storage and expenditure through integration of humoral, neural, and nutrient-related signals and cues. Many years of research have focused on the regulation of energy balance by hypothalamic neurons, but the most recent findings suggest that neurons and glial cells, such as microglia and astrocytes, in the hypothalamus actually orchestrate together several metabolic functions. Because glial cells have been described as mediators of inflammatory processes in the brain, the existence of a causal link between hypothalamic inflammation and the deregulations of feeding behavior, leading to involuntary weight loss or obesity for example, has been suggested. Several inflammatory pathways that could impair the hypothalamic control of energy balance have been studied over the years such as, among others, toll-like receptors and canonical cytokines. Yet, less studied so far, chemokines also represent interesting candidates that could link the aforementioned pathways and the activity of hypothalamic neurons. Indeed, chemokines, in addition to their role in attracting immune cells to the inflamed site, have been suggested to be capable of neuromodulation. Thus, they could disrupt cellular activity together with synthesis and/or secretion of multiple neurotransmitters/mediators involved in the maintenance of energy balance. This review discusses the different inflammatory pathways that have been identified so far in the hypothalamus in the context of feeding behavior and body weight control impairments, with a particular focus on chemokines signaling that opens a new avenue in the understanding of the major role played by inflammation in obesity.

  15. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  16. [hypothalamic Dysfunction In Obesity].

    OpenAIRE

    van de Sande-Lee, Simone; Velloso, Licio A.

    2015-01-01

    Obesity, defined as abnormal or excessive fat accumulation that may impair life quality, is one of the major public health problems worldwide. It results from an imbalance between food intake and energy expenditure. The control of energy balance in animals and humans is performed by the central nervous system (CNS) by means of neuroendocrine connections, in which circulating peripheral hormones, such as leptin and insulin, provide signals to specialized neurons of the hypothalamus reflecting ...

  17. Reductions in hypothalamic Gfap expression, glial cells and α-tanycytes in lean and hypermetabolic Gnasxl-deficient mice.

    Science.gov (United States)

    Holmes, Andrew P; Wong, Shi Quan; Pulix, Michela; Johnson, Kirsty; Horton, Niamh S; Thomas, Patricia; de Magalhães, João Pedro; Plagge, Antonius

    2016-04-14

    Neuronal and glial differentiation in the murine hypothalamus is not complete at birth, but continues over the first two weeks postnatally. Nutritional status and Leptin deficiency can influence the maturation of neuronal projections and glial patterns, and hypothalamic gliosis occurs in mouse models of obesity. Gnasxl constitutes an alternative transcript of the genomically imprinted Gnas locus and encodes a variant of the signalling protein Gαs, termed XLαs, which is expressed in defined areas of the hypothalamus. Gnasxl-deficient mice show postnatal growth retardation and undernutrition, while surviving adults remain lean and hypermetabolic with increased sympathetic nervous system (SNS) activity. Effects of this knock-out on the hypothalamic neural network have not yet been investigated. RNAseq analysis for gene expression changes in hypothalami of Gnasxl-deficient mice indicated Glial fibrillary acid protein (Gfap) expression to be significantly down-regulated in adult samples. Histological analysis confirmed a reduction in Gfap-positive glial cell numbers specifically in the hypothalamus. This reduction was observed in adult tissue samples, whereas no difference was found in hypothalami of postnatal stages, indicating an adaptation in adult Gnasxl-deficient mice to their earlier growth phenotype and hypermetabolism. Especially noticeable was a loss of many Gfap-positive α-tanycytes and their processes, which form part of the ependymal layer that lines the medial and dorsal regions of the 3(rd) ventricle, while β-tanycytes along the median eminence (ME) and infundibular recesses appeared unaffected. This was accompanied by local reductions in Vimentin and Nestin expression. Hypothalamic RNA levels of glial solute transporters were unchanged, indicating a potential compensatory up-regulation in the remaining astrocytes and tanycytes. Gnasxl deficiency does not directly affect glial development in the hypothalamus, since it is expressed in neurons, and Gfap

  18. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

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

    Science.gov (United States)

    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.

  20. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

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

    Science.gov (United States)

    Simons, Laura; Elman, Igor; Borsook, David

    2014-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

  2. 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.

  3. Tyrosine hydroxylase is short-term regulated by the ubiquitin-proteasome system in PC12 cells and hypothalamic and brainstem neurons from spontaneously hypertensive rats: possible implications in hypertension.

    Directory of Open Access Journals (Sweden)

    Nadia A Congo Carbajosa

    Full Text Available Aberrations in the ubiquitin-proteasome system (UPS are implicated in the pathogenesis of various diseases. Tyrosine hydroxylase (TH, the rate-limiting enzyme in catecholamines biosynthesis, is involved in hypertension development. In this study we investigated whether UPS regulated TH turnover in PC12 cells and hypothalamic and brainstem neurons from spontaneously hypertensive rats (SHR and whether this system was impaired in hypertension. PC12 cells were exposed to proteasome or lysosome inhibitors and TH protein level evaluated by Western blot. Lactacystin, a proteasome inhibitor, induced an increase of 86 ± 15% in TH levels after 30 min of incubation, then it started to decrease up to 6 h to reach control levels and finally it rose up to 35.2 ± 8.5% after 24 h. Bafilomycin, a lysosome inhibitor, did not alter TH protein levels during short times, but it increased TH by 92 ± 22% above basal after 6 h treatment. Before degradation proteasome substrates are labeled by conjugation with ubiquitin. Efficacy of proteasome inhibition on TH turnover was evidenced by accumulation of ubiquitinylated TH after 30 min. Further, the inhibition of proteasome increased the quantity of TH phosphorylated at Ser40, which is essential for TH activity, by 2.7 ± 0.3 fold above basal. TH protein level was upregulated in neurons from hypothalami and brainstem of SHR when the proteasome was inhibited during 30 min, supporting that neuronal TH is also short-term regulated by the proteasome. Since the increased TH levels reported in hypertension may result from proteasome dysfunction, we evaluate proteasome activity. Proteasome activity was significantly reduced by 67 ± 4% in hypothalamic and brainstem neurons from SHR while its protein levels did not change. Present findings show that TH is regulated by the UPS. The impairment in proteasome activity observed in SHR neurons may be one of the causes of the increased TH protein levels reported in hypertension.

  4. 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

  5. The hypothalamic-pituitary-adrenal and the hypothalamic- pituitary-gonadal axes interplay.

    Science.gov (United States)

    Mastorakos, George; Pavlatou, Maria G; Mizamtsidi, Maria

    2006-01-01

    Vertebrates respond to stress with activation of the hypothalamic-pituitary-adrenal (HPA) axis, the adrenergic and the autonomic nervous systems. The principal central nervous system regulators of the HPA axis are corticotropin releasing hormone (CRH) and antidiuretic hormone (AVP). Apart from in the central nervous system, CRH has been found in the adrenal medulla, ovaries, myometrium, endometrium, placenta, testis and elsewhere. The activation of the HPA axis during stress affects all body systems. The reproductive axis is inhibited by the HPA axis for the sake of saving energy. The changes to the hypothalamic-pituitary-gonadal (HPG) axis during stress are species-specific, and depend on the type and duration of the stimulus. Several conditions may be associated with altered regulation of the HPA axis. Polycystic ovary syndrome, anorexia nervosa and pregnancy in the third trimester are all characterized by HPA axis activation. In contrast, during the postpartum period, HPA axis suppression is implicated in the "postpartum blues". The actions of CRH are also essential in fetal development and neonatal survival.

  6. Altered hypothalamic function in diet-induced obesity

    National Research Council Canada - National Science Library

    Velloso, L A; Schwartz, M W

    2011-01-01

    Energy homeostasis involves a complex network of hypothalamic and extra-hypothalamic neurons that transduce hormonal, nutrient and neuronal signals into responses that ultimately match caloric intake...

  7. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  8. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  9. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  10. 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.

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

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    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. PMID:25610897

  12. 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.

  13. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Zebrafish adult-derived hypothalamic neurospheres generate gonadotropin-releasing hormone (GnRH neurons

    Directory of Open Access Journals (Sweden)

    Christian Cortés-Campos

    2015-09-01

    Full Text Available Gonadotropin-releasing hormone (GnRH is a hypothalamic decapeptide essential for fertility in vertebrates. Human male patients lacking GnRH and treated with hormone therapy can remain fertile after cessation of treatment suggesting that new GnRH neurons can be generated during adult life. We used zebrafish to investigate the neurogenic potential of the adult hypothalamus. Previously we have characterized the development of GnRH cells in the zebrafish linking genetic pathways to the differentiation of neuromodulatory and endocrine GnRH cells in specific regions of the brain. Here, we developed a new method to obtain neural progenitors from the adult hypothalamus in vitro. Using this system, we show that neurospheres derived from the adult hypothalamus can be maintained in culture and subsequently differentiate glia and neurons. Importantly, the adult derived progenitors differentiate into neurons containing GnRH and the number of cells is increased through exposure to either testosterone or GnRH, hormones used in therapeutic treatment in humans. Finally, we show in vivo that a neurogenic niche in the hypothalamus contains GnRH positive neurons. Thus, we demonstrated for the first time that neurospheres can be derived from the hypothalamus of the adult zebrafish and that these neural progenitors are capable of producing GnRH containing neurons.

  15. Inner capillary diameter of hypothalamic paraventricular nucleus of female rat increases during lactation

    Directory of Open Access Journals (Sweden)

    Cortés-Sol Albertina

    2013-01-01

    Full Text Available Abstract Background The role of the endothelial cell (EC in blood flow regulation within the central nervous system has been little studied. Here, we explored EC participation in morphological changes of the anterior hypothalamic paraventricular nucleus (PVN microvasculature of female rats at two reproductive stages with different metabolic demand (virginity and lactation. We measured the inner capillary diameter (ICD of 800 capillaries from either the magnocellular or parvocellular regions. The space occupied by neural (somas, dendrites and axons and glial, but excluding vascular elements of the neurovascular compartment was also measured in 100-μm2 sample fields of both PVN subdivisions. Results The PVN of both groups of animals showed ICDs that ranged from 3 to 10 microns. The virgin group presented mostly capillaries with small ICD, whereas the lactating females exhibited a significant increment in the percentage of capillaries with larger ICD. The space occupied by the neural and glial elements of the neurovascular compartment did not show changes with lactation. Conclusions Our findings suggest that during lactation the microvasculature of the PVN of female rats undergoes dynamic, transitory changes in blood flow as represented by an increment in the ICD through a self-cytoplasmic volume modification reflected by EC changes. A model of this process is proposed.

  16. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

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

    2015-09-01

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

  17. Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

    Science.gov (United States)

    Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

    2017-03-01

    Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

  18. Study of the neural dynamics for understanding communication in terms of complex hetero systems.

    Science.gov (United States)

    Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo

    2015-01-01

    The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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

    Energy Technology Data Exchange (ETDEWEB)

    Vargas, Lorena P [Lorena Vargas Quintero, Optic and Computer Science Group - Universidad Popular del Cesar (Colombia); Barba, Leiner; Torres, C O; Mattos, L, E-mail: vargas.lorena@yahoo.com [Optic and Computer Science Group - Popular of Cesar University, Km 12, Valledupar (Colombia)

    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.

  20. Pathophysiology and clinical characteristics of hypothalamic obesity in children and adolescents

    Directory of Open Access Journals (Sweden)

    Ja Hye Kim

    2013-12-01

    Full Text Available The hypothalamus plays a key role in the regulation of body weight by balancing the intake of food, energy expenditure, and body fat stores, as evidenced by the fact that most monogenic syndromes of morbid obesity result from mutations in genes expressed in the hypothalamus. Hypothalamic obesity is a result of impairment in the hypothalamic regulatory centers of body weight and energy expenditure, and is caused by structural damage to the hypothalamus, radiotherapy, Prader-Willi syndrome, and mutations in the LEP, LEPR, POMC, MC4R and CART genes. The pathophysiology includes loss of sensitivity to afferent peripheral humoral signals, such as leptin, dysregulated insulin secretion, and impaired activity of the sympathetic nervous system. Dysregulation of 11β-hydroxysteroid dehydrogenase 1 activity and melatonin may also have a role in the development of hypothalamic obesity. Intervention of this complex entity requires simultaneous targeting of several mechanisms that are deranged in patients with hypothalamic obesity. Despite a great deal of theoretical understanding, effective treatment for hypothalamic obesity has not yet been developed. Therefore, understanding the mechanisms that control food intake and energy homeostasis and pathophysiology of hypothalamic obesity can be the cornerstone of the development of new treatments options. Early identification of patients at-risk can relieve the severity of weight gain by the provision of dietary and behavioral modification, and antiobesity medication. This review summarizes recent advances of the pathophysiology, endocrine characteristics, and treatment strategies of hypothalamic obesity.

  1. Pathophysiology and clinical characteristics of hypothalamic obesity in children and adolescents.

    Science.gov (United States)

    Kim, Ja Hye; Choi, Jin-Ho

    2013-12-01

    The hypothalamus plays a key role in the regulation of body weight by balancing the intake of food, energy expenditure, and body fat stores, as evidenced by the fact that most monogenic syndromes of morbid obesity result from mutations in genes expressed in the hypothalamus. Hypothalamic obesity is a result of impairment in the hypothalamic regulatory centers of body weight and energy expenditure, and is caused by structural damage to the hypothalamus, radiotherapy, Prader-Willi syndrome, and mutations in the LEP, LEPR, POMC, MC4R and CART genes. The pathophysiology includes loss of sensitivity to afferent peripheral humoral signals, such as leptin, dysregulated insulin secretion, and impaired activity of the sympathetic nervous system. Dysregulation of 11β-hydroxysteroid dehydrogenase 1 activity and melatonin may also have a role in the development of hypothalamic obesity. Intervention of this complex entity requires simultaneous targeting of several mechanisms that are deranged in patients with hypothalamic obesity. Despite a great deal of theoretical understanding, effective treatment for hypothalamic obesity has not yet been developed. Therefore, understanding the mechanisms that control food intake and energy homeostasis and pathophysiology of hypothalamic obesity can be the cornerstone of the development of new treatments options. Early identification of patients at-risk can relieve the severity of weight gain by the provision of dietary and behavioral modification, and antiobesity medication. This review summarizes recent advances of the pathophysiology, endocrine characteristics, and treatment strategies of hypothalamic obesity.

  2. Effects of embryonic ethanol exposure at low doses on neuronal development, voluntary ethanol consumption and related behaviors in larval and adult zebrafish: Role of hypothalamic orexigenic peptides

    Science.gov (United States)

    Sterling, M.E.; Chang, G.-Q.; Karatayev, O.; Chang, S.Y.; Leibowitz, S.F.

    2016-01-01

    Embryonic exposure to ethanol is known to affect neurochemical systems in rodents and increase alcohol drinking and related behaviors in humans and rodents. With zebrafish emerging as a powerful tool for uncovering neural mechanisms of numerous diseases and exhibiting similarities to rodents, the present report building on our rat studies examined in zebrafish the effects of embryonic ethanol exposure on hypothalamic neurogenesis, expression of orexigenic neuropeptides, and voluntary ethanol consumption and locomotor behaviors in larval and adult zebrafish, and also effects of central neuropeptide injections on these behaviors affected by ethanol. At 24 h post-fertilization, zebrafish embryos were exposed for 2 h to ethanol, at low concentrations of 0.25% and 0.5%, in the tank water. Embryonic ethanol compared to control dose-dependently increased hypothalamic neurogenesis and the proliferation and expression of the orexigenic peptides, galanin (GAL) and orexin (OX), in the anterior hypothalamus. These changes in hypothalamic peptide neurons were accompanied by an increase in voluntary consumption of 10% ethanol-gelatin and in novelty-induced locomotor and exploratory behavior in adult zebrafish and locomotor activity in larvae. After intracerebroventricular injection, these peptides compared to vehicle had specific effects on these behaviors altered by ethanol, with GAL stimulating consumption of 10% ethanol-gelatin more than plain gelatin food and OX stimulating novelty-induced locomotor behavior while increasing intake of food and ethanol equally. These results, similar to those obtained in rats, suggest that the ethanol-induced increase in genesis and expression of these hypothalamic peptide neurons contribute to the behavioral changes induced by embryonic exposure to ethanol. PMID:26778786

  3. Involvement of hypothalamic AMP-activated protein kinase in leptin-induced sympathetic nerve activation.

    Science.gov (United States)

    Tanida, Mamoru; Yamamoto, Naoki; Shibamoto, Toshishige; Rahmouni, Kamal

    2013-01-01

    In mammals, leptin released from the white adipose tissue acts on the central nervous system to control feeding behavior, cardiovascular function, and energy metabolism. Central leptin activates sympathetic nerves that innervate the kidney, adipose tissue, and some abdominal organs in rats. AMP-activated protein kinase (AMPK) is essential in the intracellular signaling pathway involving the activation of leptin receptors (ObRb). We investigated the potential of AMPKα2 in the sympathetic effects of leptin using in vivo siRNA injection to knockdown AMPKα2 in rats, to produce reduced hypothalamic AMPKα2 expression. Leptin effects on body weight, food intake, and blood FFA levels were eliminated in AMPKα2 siRNA-treated rats. Leptin-evoked enhancements of the sympathetic nerve outflows to the kidney, brown and white adipose tissues were attenuated in AMPKα2 siRNA-treated rats. To check whether AMPKα2 was specific to sympathetic changes induced by leptin, we examined the effects of injecting MT-II, a melanocortin-3 and -4 receptor agonist, on the sympathetic nerve outflows to the kidney and adipose tissue. MT-II-induced sympatho-excitation in the kidney was unchanged in AMPKα2 siRNA-treated rats. However, responses of neural activities involving adipose tissue to MT-II were attenuated in AMPKα2 siRNA-treated rats. These results suggest that hypothalamic AMPKα2 is involved not only in appetite and body weight regulation but also in the regulation of sympathetic nerve discharges to the kidney and adipose tissue. Thus, AMPK might function not only as an energy sensor, but as a key molecule in the cardiovascular, thermogenic, and lipolytic effects of leptin through the sympathetic nervous system.

  4. Involvement of hypothalamic AMP-activated protein kinase in leptin-induced sympathetic nerve activation.

    Directory of Open Access Journals (Sweden)

    Mamoru Tanida

    Full Text Available In mammals, leptin released from the white adipose tissue acts on the central nervous system to control feeding behavior, cardiovascular function, and energy metabolism. Central leptin activates sympathetic nerves that innervate the kidney, adipose tissue, and some abdominal organs in rats. AMP-activated protein kinase (AMPK is essential in the intracellular signaling pathway involving the activation of leptin receptors (ObRb. We investigated the potential of AMPKα2 in the sympathetic effects of leptin using in vivo siRNA injection to knockdown AMPKα2 in rats, to produce reduced hypothalamic AMPKα2 expression. Leptin effects on body weight, food intake, and blood FFA levels were eliminated in AMPKα2 siRNA-treated rats. Leptin-evoked enhancements of the sympathetic nerve outflows to the kidney, brown and white adipose tissues were attenuated in AMPKα2 siRNA-treated rats. To check whether AMPKα2 was specific to sympathetic changes induced by leptin, we examined the effects of injecting MT-II, a melanocortin-3 and -4 receptor agonist, on the sympathetic nerve outflows to the kidney and adipose tissue. MT-II-induced sympatho-excitation in the kidney was unchanged in AMPKα2 siRNA-treated rats. However, responses of neural activities involving adipose tissue to MT-II were attenuated in AMPKα2 siRNA-treated rats. These results suggest that hypothalamic AMPKα2 is involved not only in appetite and body weight regulation but also in the regulation of sympathetic nerve discharges to the kidney and adipose tissue. Thus, AMPK might function not only as an energy sensor, but as a key molecule in the cardiovascular, thermogenic, and lipolytic effects of leptin through the sympathetic nervous system.

  5. Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

    Science.gov (United States)

    Shepanski, J. F.; Macy, S. A.

    1987-01-01

    A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

  6. Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

    Science.gov (United States)

    Fusaro, Robert L.

    1998-01-01

    Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

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

  9. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.

    Science.gov (United States)

    Ohlsson, Mattias

    2004-01-01

    This paper presents a computer-based decision support system for automated interpretation of diagnostic heart images (called WeAidU), which is made available via the Internet. The system is based on image processing techniques, artificial neural networks (ANNs) and large well-validated medical databases. We present results using artificial neural networks, and compare with two other classification methods, on a retrospective data set containing 1320 images from the clinical routine. The performance of the artificial neural networks detecting infarction and ischemia in different parts of the heart, measured as areas under the receiver operating characteristic curves, is in the range 0.83-0.96. These results indicate a high potential for the tool as a clinical decision support system.

  10. 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...

  11. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...

  12. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  13. Neonatal ghrelin programs development of hypothalamic feeding circuits

    Science.gov (United States)

    Steculorum, Sophie M.; Collden, Gustav; Coupe, Berengere; Croizier, Sophie; Lockie, Sarah; Andrews, Zane B.; Jarosch, Florian; Klussmann, Sven; Bouret, Sebastien G.

    2015-01-01

    A complex neural network regulates body weight and energy balance, and dysfunction in the communication between the gut and this neural network is associated with metabolic diseases, such as obesity. The stomach-derived hormone ghrelin stimulates appetite through interactions with neurons in the arcuate nucleus of the hypothalamus (ARH). Here, we evaluated the physiological and neurobiological contribution of ghrelin during development by specifically blocking ghrelin action during early postnatal development in mice. Ghrelin blockade in neonatal mice resulted in enhanced ARH neural projections and long-term metabolic effects, including increased body weight, visceral fat, and blood glucose levels and decreased leptin sensitivity. In addition, chronic administration of ghrelin during postnatal life impaired the normal development of ARH projections and caused metabolic dysfunction. Consistent with these observations, direct exposure of postnatal ARH neuronal explants to ghrelin blunted axonal growth and blocked the neurotrophic effect of the adipocyte-derived hormone leptin. Moreover, chronic ghrelin exposure in neonatal mice also attenuated leptin-induced STAT3 signaling in ARH neurons. Collectively, these data reveal that ghrelin plays an inhibitory role in the development of hypothalamic neural circuits and suggest that proper expression of ghrelin during neonatal life is pivotal for lifelong metabolic regulation. PMID:25607843

  14. Prediction of Phase Behavior in Microemulsion Systems Using Artificial Neural Networks

    Science.gov (United States)

    Richardson; Mbanefo; Aboofazeli; Lawrence; Barlow

    1997-03-15

    Preliminary investigations have been conducted to assess the potential for using (back-propagation, feed-forward) artificial neural networks to predict the phase behavior of quaternary microemulsion-forming systems, with a view to employing this type of methodology in the evaluation of novel cosurfactants for the formulation of pharmaceutically acceptable drug-delivery systems. The data employed in training the neural networks related to microemulsion systems containing lecithin, isopropyl myristate, and water, together with different types of cosurfactants, including short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers. Previously unpublished phase diagrams are presented for four systems involving the cosurfactants 2-methyl-2-butanol, 2-methyl-1-propanol, 2-methyl-1-butanol, and isopropanol, which, along with eight other published sets of data, are used to test the predictive ability of the trained networks. The pseudo-ternary phase diagrams for these systems are predicted using only four computed physicochemical properties for the cosurfactants involved. The artificial neural networks are shown to be highly successful in predicting phase behavior for these systems, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. The conclusion is reached that artificial neural networks can provide useful tools for the development of microemulsion-based drug-delivery systems.

  15. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  16. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....

  17. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  18. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Neural maps in insect versus vertebrate auditory systems.

    Science.gov (United States)

    Hildebrandt, K Jannis

    2014-02-01

    The convergent evolution of hearing in insects and vertebrates raises the question about similarity of the central representation of sound in these distant animal groups. Topographic representations of spectral, spatial and temporal cues have been widely described in mammals, but evidence for such maps is scarce in insects. Recent data on insect sound encoding provides evidence for an early integration of sound parameters to form highly-specific representation that predict behavioral output. In mammals, new studies investigating neural representation of perceptual features in behaving animals allow asking similar questions. A comparative approach may help in understanding principles underlying the formation of perceptual categories and behavioral plasticity. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  1. Neural systems for evaluating speaker (Un)believability.

    Science.gov (United States)

    Jiang, Xiaoming; Sanford, Ryan; Pell, Marc D

    2017-04-30

    Our voice provides salient cues about how confident we sound, which promotes inferences about how believable we are. However, the neural mechanisms involved in these social inferences are largely unknown. Employing functional magnetic resonance imaging, we examined the brain networks and individual differences underlying the evaluation of speaker believability from vocal expressions. Participants (n = 26) listened to statements produced in a confident, unconfident, or "prosodically unmarked" (neutral) voice, and judged how believable the speaker was on a 4-point scale. We found frontal-temporal networks were activated for different levels of confidence, with the left superior and inferior frontal gyrus more activated for confident statements, the right superior temporal gyrus for unconfident expressions, and bilateral cerebellum for statements in a neutral voice. Based on listener's believability judgment, we observed increased activation in the right superior parietal lobule (SPL) associated with higher believability, while increased left posterior central gyrus (PoCG) was associated with less believability. A psychophysiological interaction analysis found that the anterior cingulate cortex and bilateral caudate were connected to the right SPL when higher believability judgments were made, while supplementary motor area was connected with the left PoCG when lower believability judgments were made. Personal characteristics, such as interpersonal reactivity and the individual tendency to trust others, modulated the brain activations and the functional connectivity when making believability judgments. In sum, our data pinpoint neural mechanisms that are involved when inferring one's believability from a speaker's voice and establish ways that these mechanisms are modulated by individual characteristics of a listener. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  2. Differentiation of hypothalamic-like neurons from human pluripotent stem cells

    OpenAIRE

    Wang, Liheng; Meece, Kana; Damian J Williams; Lo, Kinyui Alice; Zimmer, Matthew; Heinrich, Garrett; Martin Carli, Jayne; LeDuc, Charles A.; Sun, Lei; Zeltser, Lori M.; Freeby, Matthew; Goland, Robin; Stephen H. Tsang; Wardlaw, Sharon L.; Egli, Dieter

    2015-01-01

    The hypothalamus is the central regulator of systemic energy homeostasis, and its dysfunction can result in extreme body weight alterations. Insights into the complex cellular physiology of this region are critical to the understanding of obesity pathogenesis; however, human hypothalamic cells are largely inaccessible for direct study. Here, we developed a protocol for efficient generation of hypothalamic neurons from human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs...

  3. RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System

    Directory of Open Access Journals (Sweden)

    Haizhong Chen

    2013-01-01

    Full Text Available This paper presents a radial basis function (RBF neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.

  4. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

    Science.gov (United States)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg

    2011-04-01

    Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.

  5. Prenatal bisphenol a exposure and dysregulation of infant hypothalamic-pituitary-adrenal axis function: findings from the APrON cohort study

    National Research Council Canada - National Science Library

    Gerald F Giesbrecht; Maede Ejaredar; Jiaying Liu; Jenna Thomas; Nicole Letourneau; Tavis Campbell; Jonathan W Martin; Deborah Dewey

    2017-01-01

    Background Animal models show that prenatal bisphenol A (BPA) exposure leads to sexually dimorphic disruption of the neuroendocrine system in offspring, including the hypothalamic-pituitary-adrenal (HPA...

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

    Science.gov (United States)

    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.

  7. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation

    Directory of Open Access Journals (Sweden)

    H.M. Endedijk

    2017-04-01

    Full Text Available Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other’s actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children’s interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction.

  8. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. 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.

  10. 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.

  11. Genetic manipulation of specific neural circuits by use of a viral vector system.

    Science.gov (United States)

    Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto

    2017-01-05

    To understand the mechanisms underlying higher brain functions, we need to analyze the roles of specific neuronal pathways or cell types forming the complex neural networks. In the neuroscience field, the transgenic approach has provided a useful gene engineering tool for experimental studies of neural functions. The conventional transgenic technique requires the appropriate promoter regions that drive a neuronal type-specific gene expression, but the promoter sequences specifically functioning in each neuronal type are limited. Previously, we developed novel types of lentiviral vectors showing high efficiency of retrograde gene transfer in the central nervous system, termed highly efficient retrograde gene transfer (HiRet) vector and neuron-specific retrograde gene transfer (NeuRet) vector. The HiRet and NeuRet vectors enable genetical manipulation of specific neural pathways in diverse model animals in combination with conditional cell targeting, synaptic transmission silencing, and gene expression systems. These newly developed vectors provide powerful experimental strategies to investigate, more precisely, the machineries exerting various neural functions. In this review, we give an outline of the HiRet and NeuRet vectors and describe recent representative applications of these viral vectors for studies on neural circuits.

  12. Role of leptin in energy expenditure: the hypothalamic perspective.

    Science.gov (United States)

    Pandit, R; Beerens, S; Adan, R A H

    2017-06-01

    The adipocyte-derived hormone leptin is a peripheral signal that informs the brain about the metabolic status of an organism. Although traditionally viewed as an appetite-suppressing hormone, studies in the past decade have highlighted the role of leptin in energy expenditure. Leptin has been shown to increase energy expenditure in particular through its effects on the cardiovascular system and brown adipose tissue (BAT) thermogenesis via the hypothalamus. The current review summarizes the role of leptin signaling in various hypothalamic nuclei and its effects on the sympathetic nervous system to influence blood pressure, heart rate, and BAT thermogenesis. Specifically, the role of leptin signaling on three different hypothalamic nuclei, the dorsomedial hypothalamus, the ventromedial hypothalamus, and the arcuate nucleus, is reviewed. It is known that all of these brain regions influence the sympathetic nervous system activity and thereby regulate BAT thermogenesis and the cardiovascular system. Thus the current work focuses on how leptin signaling in specific neuronal populations within these hypothalamic nuclei influences certain aspects of energy expenditure. Copyright © 2017 the American Physiological Society.

  13. 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.

  14. Networked neural spheroid by neuro-bundle mimicking nervous system created by topology effect.

    Science.gov (United States)

    Jeong, Gi Seok; Chang, Joon Young; Park, Ji Soo; Lee, Seung-A; Park, DoYeun; Woo, Junsung; An, Heeyoung; Lee, C Justin; Lee, Sang-Hoon

    2015-03-22

    In most animals, the nervous system consists of the central nervous system (CNS) and the peripheral nervous system (PNS), the latter of which connects the CNS to all parts of the body. Damage and/or malfunction of the nervous system causes serious pathologies, including neurodegenerative disorders, spinal cord injury, and Alzheimer's disease. Thus, not surprising, considerable research effort, both in vivo and in vitro, has been devoted to studying the nervous system and signal transmission through it. However, conventional in vitro cell culture systems do not enable control over diverse aspects of the neural microenvironment. Moreover, formation of certain nervous system growth patterns in vitro remains a challenge. In this study, we developed a deep hemispherical, microchannel-networked, concave array system and applied it to generate three-dimensional nerve-like neural bundles. The deep hemicylindrical channel network was easily fabricated by exploiting the meniscus induced by the surface tension of a liquid poly(dimethylsiloxane) (PDMS) prepolymer. Neurospheroids spontaneously aggregated in each deep concave microwell and were networked to neighboring spheroids through the deep hemicylindrical channel. Notably, two types of satellite spheroids also formed in deep hemispherical microchannels through self-aggregation and acted as an anchoring point to enhance formation of nerve-like networks with neighboring spheroids. During neural-network formation, neural progenitor cells successfully differentiated into glial and neuronal cells. These cells secreted laminin, forming an extracellular matrix around the host and satellite spheroids. Electrical stimuli were transmitted between networked neurospheroids in the resulting nerve-like neural bundle, as detected by imaging Ca(2+) signals in responding cells.

  15. Differential sensitivity to nicotine among hypothalamic magnocellular neurons

    DEFF Research Database (Denmark)

    Mikkelsen, J D; Jacobsen, Julie; Kiss, Adrian Emil

    2012-01-01

    The magnocellular neurons in the hypothalamic paraventricular (PVN) and supraoptic nuclei (SON) either contain vasopressin or oxytocin. Even though both hormones are released after systemic administration of nicotine, the mechanism through which the two populations of neurons are activated...... is not known. This study was carried out in the rat to investigate the effect of increasing doses of nicotine on subsets of magnocellular neurons containing either oxytocin or vasopressin....

  16. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  17. Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems

    Directory of Open Access Journals (Sweden)

    Cong-Hui Huang

    2014-12-01

    Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.

  18. Recent advances in understanding the role of the hypothalamic circuit during aggression

    OpenAIRE

    Falkner, Annegret L.; Lin, Dayu

    2014-01-01

    The hypothalamus was first implicated in the classic “fight or flight” response nearly a century ago, and since then, many important strides have been made in understanding both the circuitry and the neural dynamics underlying the generation of these behaviors. In this review, we will focus on the role of the hypothalamus in aggression, paying particular attention to recent advances in the field that have allowed for functional identification of relevant hypothalamic subnuclei. Recent progres...

  19. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  20. Hypothalamic hamartoma: Is the epileptogenic zone always hypothalamic? Arguments for independent (third stage) secondary epileptogenesis

    National Research Council Canada - National Science Library

    Scholly, Julia; Valenti, Maria‐Paola; Staack, Anke M; Strobl, Karl; Bast, Thomas; Kehrli, Pierre; Steinhoff, Bernhard J; Hirsch, Edouard

    2013-01-01

    Gelastic seizures associated with hypothalamic hamartomas ( HH s) are a clinicoradiologic syndrome presenting with a variety of symptoms, including pharmacoresistant epilepsy with multiple seizure types, electroencephalography ( EEG...

  1. How instructed knowledge modulates the neural systems of reward learning.

    Science.gov (United States)

    Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A

    2011-01-04

    Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

  2. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  3. Neural systemic impairment from whole-body vibration.

    Science.gov (United States)

    Yan, Ji-Geng; Zhang, Lin-ling; Agresti, Michael; LoGiudice, John; Sanger, James R; Matloub, Hani S; Havlik, Robert

    2015-05-01

    Insidious brain microinjury from motor vehicle-induced whole-body vibration (WBV) has not yet been investigated. For a long time we have believed that WBV would cause cumulative brain microinjury and impair cerebral function, which suggests an important risk factor for motor vehicle accidents and secondary cerebral vascular diseases. Fifty-six Sprague-Dawley rats were divided into seven groups (n = 8): 1) 2-week normal control group, 2) 2-week sham control group (restrained in the tube without vibration), 3) 2-week vibration group (exposed to whole-body vibration at 30 Hz and 0.5g acceleration for 4 hr/day, 5 days/week, for 2 weeks), 4) 4-week sham control group, 5) 4-week vibration group, 6) 8-week sham control group, and 7) 8-week vibration group. At the end point, all rats were evaluated in behavior, physiological, and brain histopathological studies. The cerebral injury from WBV is a cumulative process starting with vasospasm squeezing of the endothelial cells, followed by constriction of the cerebral arteries. After the 4-week vibration, brain neuron apoptosis started. After the 8-week vibration, vacuoles increased further in the brain arteries. Brain capillary walls thickened, mean neuron size was obviously reduced, neuron necrosis became prominent, and wide-ranging chronic cerebral edema was seen. These pathological findings are strongly correlated with neural functional impairments. © 2014 Wiley Periodicals, Inc.

  4. Metabolic Actions of Hypothalamic SIRT1

    Science.gov (United States)

    Coppari, Roberto

    2012-01-01

    The hypothalamus is a small structure located in the ventral diencephalon. Hypothalamic neurons sense changes in circulating metabolic cues (e.g.: leptin, insulin, glucose), and coordinate responses aimed at maintaining normal body weight and glucose homeostasis. Recent findings indicate that a nicotinamide adenine dinucleotide (NAD+)-dependent protein deacetylase (namely, SIRT1) expressed by hypothalamic neurons is crucial for mounting responses against diet-induced obesity and type 2 diabetes mellitus (T2DM). Here, the repercussions of these findings will be discussed and particular emphasis will be given to the potential exploitation of hypothalamic SIRT1 as a target for the treatment of the rapidly-spreading metabolic disorders of obesity and T2DM. The possible roles of hypothalamic SIRT1 on regulating metabolic ageing processes will also be addressed. PMID:22382036

  5. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  6. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  7. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.

  8. Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning

    NARCIS (Netherlands)

    Petkov, Nikolay

    1995-01-01

    A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input

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

    NARCIS (Netherlands)

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

    2006-01-01

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

  10. Comparison of different computer models of the neural control system of the lower urinary tract

    NARCIS (Netherlands)

    van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.

    2000-01-01

    This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of

  11. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  12. 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

    . Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution......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...

  13. Building an Early Warning System for Crude Oil Price Using Neural Network

    Directory of Open Access Journals (Sweden)

    Wonho Song

    2010-12-01

    Full Text Available In this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning sysIn this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning system. Most early warning systems are built based on the signaling approach. In this paper, we show that the neural network models are more flexible and have greater potential as EWS than the signaling approach. Third, we allow the multi-level crisis index. Previous models allowed only a zero/one crisis index whereas our model permits as many levels as possible. With this new model, we try to answer whether the oil price collapse following the historical peak in 2008 was predictable. We compare the results from the NN model with those from the ordered probit (OP model, and show that the oil price crisis and the following crash were predictable by the NN model, but not by the OP model.

  14. A neural network approach to fault detection in spacecraft attitude determination and control systems

    Science.gov (United States)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  15. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 – 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  16. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  17. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    Science.gov (United States)

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  19. Hypothalamic adipsic syndrome: diagnosis and management.

    Science.gov (United States)

    Ball, S G; Vaidja, B; Baylis, P H

    1997-10-01

    Patients with hypothalamic adipsic syndrome, especially in conjunction with diabetes insipidus, pose management difficulties. They are at risk of both under- and over-hydration. We present 4 patients with hypothalamic adipsic syndromes, due to different causes, illustrating the practical difficulties encountered in this condition. The principles of management, with a sliding scale of water intake related to changes in daily body weight, are discussed.

  20. Neuropeptide Y stimulates autophagy in hypothalamic neurons.

    Science.gov (United States)

    Aveleira, Célia A; Botelho, Mariana; Carmo-Silva, Sara; Pascoal, Jorge F; Ferreira-Marques, Marisa; Nóbrega, Clévio; Cortes, Luísa; Valero, Jorge; Sousa-Ferreira, Lígia; Álvaro, Ana R; Santana, Magda; Kügler, Sebastian; Pereira de Almeida, Luís; Cavadas, Cláudia

    2015-03-31

    Aging is characterized by autophagy impairment that contributes to age-related disease aggravation. Moreover, it was described that the hypothalamus is a critical brain area for whole-body aging development and has impact on lifespan. Neuropeptide Y (NPY) is one of the major neuropeptides present in the hypothalamus, and it has been shown that, in aged animals, the hypothalamic NPY levels decrease. Because caloric restriction (CR) delays aging, at least in part, by stimulating autophagy, and also increases hypothalamic NPY levels, we hypothesized that NPY could have a relevant role on autophagy modulation in the hypothalamus. Therefore, the aim of this study was to investigate the role of NPY on autophagy in the hypothalamus. Using both hypothalamic neuronal in vitro models and mice overexpressing NPY in the hypothalamus, we observed that NPY stimulates autophagy in the hypothalamus. Mechanistically, in rodent hypothalamic neurons, NPY increases autophagy through the activation of NPY Y1 and Y5 receptors, and this effect is tightly associated with the concerted activation of PI3K, MEK/ERK, and PKA signaling pathways. Modulation of hypothalamic NPY levels may be considered a potential strategy to produce protective effects against hypothalamic impairments associated with age and to delay aging.

  1. Transcriptional profiling of fetal hypothalamic TRH neurons.

    Science.gov (United States)

    Guerra-Crespo, Magdalena; Pérez-Monter, Carlos; Janga, Sarath Chandra; Castillo-Ramírez, Santiago; Gutiérrez-Rios, Rosa María; Joseph-Bravo, Patricia; Pérez-Martínez, Leonor; Charli, Jean-Louis

    2011-05-10

    During murine hypothalamic development, different neuroendocrine cell phenotypes are generated in overlapping periods; this suggests that cell-type specific developmental programs operate to achieve complete maturation. A balance between programs that include cell proliferation, cell cycle withdrawal as well as epigenetic regulation of gene expression characterizes neurogenesis. Thyrotropin releasing hormone (TRH) is a peptide that regulates energy homeostasis and autonomic responses. To better understand the molecular mechanisms underlying TRH neuron development, we performed a genome wide study of its transcriptome during fetal hypothalamic development. In primary cultures, TRH cells constitute 2% of the total fetal hypothalamic cell population. To purify these cells, we took advantage of the fact that the segment spanning -774 to +84 bp of the Trh gene regulatory region confers specific expression of the green fluorescent protein (GFP) in the TRH cells. Transfected TRH cells were purified by fluorescence activated cell sorting, various cell preparations pooled, and their transcriptome compared to that of GFP- hypothalamic cells. TRH cells undergoing the terminal phase of differentiation, expressed genes implicated in protein biosynthesis, intracellular signaling and transcriptional control. Among the transcription-associated transcripts, we identified the transcription factors Klf4, Klf10 and Atf3, which were previously uncharacterized within the hypothalamus. To our knowledge, this is one of the first reports identifying transcripts with a potentially important role during the development of a specific hypothalamic neuronal phenotype. This genome-scale study forms a rational foundation for identifying genes that might participate in the development and function of hypothalamic TRH neurons.

  2. Targeted drug delivery system to neural cells utilizes the nicotinic acetylcholine receptor.

    Science.gov (United States)

    Huey, Rachel; O'Hagan, Barry; McCarron, Paul; Hawthorne, Susan

    2017-06-15

    Drug delivery to the brain is still a major challenge in the field of therapeutics, especially for large and hydrophilic compounds. In order to achieve drug delivery of therapeutic concentration in the central nervous system, the problematic blood brain barrier (BBB) must be overcome. This work presents the formulation of a targeted nanoparticle-based drug delivery system using a specific neural cell targeting ligand, rabies virus derived peptide (RDP). Characterization studies revealed that RDP could be conjugated to drug-loaded PLGA nanoparticles of average diameter 257.10±22.39nm and zeta potential of -5.51±0.73mV. In vitro studies showed that addition of RDP to nanoparticles enhanced drug accumulation in a neural cell line specifically as opposed to non-neural cell lines. It was revealed that this drug delivery system is reliant upon nicotinic acetylcholine receptor (nAChR) function for RDP-facilitated effects, supporting a cellular uptake mechanism of action. The specific neural cell targeting capabilities of RDP via the nAChR offers a non-toxic, non-invasive and promising approach to the delivery of therapeutics to the brain. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  3. CITY TRANSPORT SYSTEM ECOLOGICAL STATE FORECASTING WITH THE USE OF NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Andrey Lyamzin

    2017-11-01

    Full Text Available Purpose: The purpose of this work is to develop an effective model for city transport system ecological state assessment using neural networks general concept. Methods: The proposed model is based on two neural networks work, taking into account the traffic density effect and the transit capacity level on urban areas. Results: Based on the synthesis of the fuzzy sets theory and neural networks basic principles, the city transport system ecological state assessing model is developed. The graphical representation of the model is given. A forecast reliability high degree is provided even at low learning rates and high dynamics of changing statistical data in the city transit traffic conditions. Conclusions: The use of fuzzy neural networks makes it possible to state a complete correspondence between fuzzy inference procedure mathematical representation and the urban transport system structure. The proposed model allows to formulate well-defined environmental guidelines when making decisions in the transit traffic field, taking into account the interests of enterprises, transport and the urban population, with the subsequent distribution of traffic flows in time and geographical space of the city industrial areas.

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

    Science.gov (United States)

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

    2016-01-01

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

  5. An Intelligent Active Video Surveillance System Based on the Integration of Virtual Neural Sensors and BDI Agents

    Science.gov (United States)

    Gregorio, Massimo De

    In this paper we present an intelligent active video surveillance system currently adopted in two different application domains: railway tunnels and outdoor storage areas. The system takes advantages of the integration of Artificial Neural Networks (ANN) and symbolic Artificial Intelligence (AI). This hybrid system is formed by virtual neural sensors (implemented as WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from a computational and hardware point of view, and rather robust in performances. The system works on different scenarios and in difficult light conditions.

  6. Rapid sensing of l-leucine by human and murine hypothalamic neurons: Neurochemical and mechanistic insights.

    Science.gov (United States)

    Heeley, Nicholas; Kirwan, Peter; Darwish, Tamana; Arnaud, Marion; Evans, Mark L; Merkle, Florian T; Reimann, Frank; Gribble, Fiona M; Blouet, Clemence

    2018-02-07

    Dietary proteins are sensed by hypothalamic neurons and strongly influence multiple aspects of metabolic health, including appetite, weight gain, and adiposity. However, little is known about the mechanisms by which hypothalamic neural circuits controlling behavior and metabolism sense protein availability. The aim of this study is to characterize how neurons from the mediobasal hypothalamus respond to a signal of protein availability: the amino acid l-leucine. We used primary cultures of post-weaning murine mediobasal hypothalamic neurons, hypothalamic neurons derived from human induced pluripotent stem cells, and calcium imaging to characterize rapid neuronal responses to physiological changes in extracellular l-Leucine concentration. A neurochemically diverse subset of both mouse and human hypothalamic neurons responded rapidly to l-leucine. Consistent with l-leucine's anorexigenic role, we found that 25% of mouse MBH POMC neurons were activated by l-leucine. 10% of MBH NPY neurons were inhibited by l-leucine, and leucine rapidly reduced AGRP secretion, providing a mechanism for the rapid leucine-induced inhibition of foraging behavior in rodents. Surprisingly, none of the candidate mechanisms previously implicated in hypothalamic leucine sensing (K ATP channels, mTORC1 signaling, amino-acid decarboxylation) were involved in the acute activity changes produced by l-leucine. Instead, our data indicate that leucine-induced neuronal activation involves a plasma membrane Ca 2+ channel, whereas leucine-induced neuronal inhibition is mediated by inhibition of a store-operated Ca 2+ current. A subset of neurons in the mediobasal hypothalamus rapidly respond to physiological changes in extracellular leucine concentration. Leucine can produce both increases and decreases in neuronal Ca 2+ concentrations in a neurochemically-diverse group of neurons, including some POMC and NPY/AGRP neurons. Our data reveal that leucine can signal through novel mechanisms to rapidly

  7. 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.

  8. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

  9. Prediction of a model enzymatic acidolysis system using neural networks

    Directory of Open Access Journals (Sweden)

    Güven, Aytaç

    2008-12-01

    Full Text Available A model for the acidolysis of trinolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase was presented in this study. A neural networks (NN based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO 1,3-dipalmitoyl-2-oleoyl-glycerol (POP and triolein (OOO. Substrate ratio (SR, reaction temperature (T and reaction time (t were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, wass able to predict the reaction products concentration with a mean square error (MSE of less than 1.5 and R2 of 0.999. and explicit formulation of the proposed NN is presented. Considerable good performance is achieved in modeling the acidolysis reaction using neuronal networks.En este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO, 1,3-dipalmitoil-2-oleoil-glicerol (POP y trioleina (OOO. Se han usado como parámetros de entrada: la proporción del sustrato (SR, la temperatura de reacción (T y el tiempo de reacción (t. La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE de menos de 1.5 y una R2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de

  10. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

    Directory of Open Access Journals (Sweden)

    Nasser Talebi

    2014-01-01

    Full Text Available 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.

  11. 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.

  12. Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

    Directory of Open Access Journals (Sweden)

    Yiyong Gou

    2017-04-01

    Full Text Available A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output (MIMO aeroelastic system in the presence of wind gust, system uncertainties, and input nonlinearities consisting of input saturation and dead-zone. In regard to the input nonlinearities, the right inverse function block of the dead-zone is added before the input nonlinearities, which simplifies the input nonlinearities into an equivalent input saturation. To deal with the equivalent input saturation, an auxiliary error system is designed to compensate for the impact of the input saturation. Meanwhile, uncertainties in pitch stiffness, plunge stiffness, and pitch damping are all considered, and radial basis function neural networks (RBFNNs are applied to approximate the system uncertainties. In combination with the designed auxiliary error system and the backstepping control technique, a constrained adaptive neural network controller is designed, and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method. Finally, extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust, system uncertainties, and input nonlinearities.

  13. 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.

  14. 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.

  15. Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

    Science.gov (United States)

    Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

    2014-01-01

    An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

  16. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

  17. Hypothalamic hamartoma: Neuropathology and epileptogenesis.

    Science.gov (United States)

    Kerrigan, John F; Parsons, Angela; Tsang, Candy; Simeone, Kristina; Coons, Stephen; Wu, Jie

    2017-06-01

    Hypothalamic hamartomas (HHs) are congenital malformations of the ventral hypothalamus resulting in treatment-resistant epilepsy and are intrinsically epileptogenic for the gelastic seizures that are the hallmark symptom of this disorder. This paper reviews the neuropathologic features of HHs associated with epilepsy, with an emphasis on characterizing neuron phenotypes and an ultimate goal of understanding the cellular model of ictogenesis occurring locally within this tissue. We also present previously unpublished findings on Golgi staining of HH. The microarchitecture of HH is relatively simple, with nodular clusters of neurons that vary in size and abundance with poorly defined boundaries. Approximately 80-90% of HH neurons have an interneuron-like phenotype with small, round soma and short, unbranched processes that lack spines. These neurons express glutamic acid decarboxylase and likely utilize γ-aminobutyric acid (GABA) as their primary neurotransmitter. They have intrinsic membrane properties that lead to spontaneous pacemaker-like firing activity. The remaining HH neurons are large cells with pleomorphic, often pyramidal, soma and dendrites that are more likely to be branched and have spines. These neurons appear to be excitatory, projection-type neurons, and have the functionally immature behavior of depolarizing and firing in response to GABA ligands. We hypothesize that the irregular neuronal clusters are the functional unit for ictogenesis. Further research to define and characterize these local networks is required to fully understand the cellular mechanisms responsible for gelastic seizures. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

  18. Increasing fatty acid oxidation remodels the hypothalamic neurometabolome to mitigate stress and inflammation.

    Directory of Open Access Journals (Sweden)

    Joseph W McFadden

    Full Text Available Modification of hypothalamic fatty acid (FA metabolism can improve energy homeostasis and prevent hyperphagia and excessive weight gain in diet-induced obesity (DIO from a diet high in saturated fatty acids. We have shown previously that C75, a stimulator of carnitine palmitoyl transferase-1 (CPT-1 and fatty acid oxidation (FAOx, exerts at least some of its hypophagic effects via neuronal mechanisms in the hypothalamus. In the present work, we characterized the effects of C75 and another anorexigenic compound, the glycerol-3-phosphate acyltransferase (GPAT inhibitor FSG67, on FA metabolism, metabolomics profiles, and metabolic stress responses in cultured hypothalamic neurons and hypothalamic neuronal cell lines during lipid excess with palmitate. Both compounds enhanced palmitate oxidation, increased ATP, and inactivated AMP-activated protein kinase (AMPK in hypothalamic neurons in vitro. Lipidomics and untargeted metabolomics revealed that enhanced catabolism of FA decreased palmitate availability and prevented the production of fatty acylglycerols, ceramides, and cholesterol esters, lipids that are associated with lipotoxicity-provoked metabolic stress. This improved metabolic signature was accompanied by increased levels of reactive oxygen species (ROS, and yet favorable changes in oxidative stress, overt ER stress, and inflammation. We propose that enhancing FAOx in hypothalamic neurons exposed to excess lipids promotes metabolic remodeling that reduces local inflammatory and cell stress responses. This shift would restore mitochondrial function such that increased FAOx can produce hypothalamic neuronal ATP and lead to decreased food intake and body weight to improve systemic metabolism.

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

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

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

  20. Central Neural Control of the Cardiovascular System: Current Perspectives

    Science.gov (United States)

    Dampney, Roger A. L.

    2016-01-01

    This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system.…

  1. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to

  2. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Friehs, Gerhard M.; Black, Michael J.

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (click 2-D cursor control of a personal computer. PMID:21278024

  3. 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.

  4. High-dimensional neural network potentials for multicomponent systems: First applications to zinc oxide

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Maschke, Marcus; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2010-07-01

    Recently, artificial neural networks (NN) trained to first-principles data have shown to provide accurate potential energy surfaces for systems containing a single atomic species. In this work we present an extension of the NN approach to multicomponent systems by introducing physically motivated terms to deal with long-range interactions. This is a necessary condition for studying binary systems and general multicomponent systems with significant charge transfer. The capabilities of the method are demonstrated for crystal structures, amorphous structures, clusters, and surfaces of zinc oxide as a benchmark system. We show that the predicted energies and forces are in excellent agreement with reference density-functional theory calculations.

  5. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  6. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  7. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  8. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  9. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  10. Simulation and stability analysis of neural network based control scheme for switched linear systems.

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

    This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  12. Robust MPC for a non-linear system - a neural network approach

    Science.gov (United States)

    Luzar, Marcel; Witczak, Marcin

    2014-12-01

    The aim of the paper is to design a robust actuator fault-tolerant control for a non-linear discrete-time system. Considered system is described by the Linear Parameter-Varying (LPV) model obtained with recurrent neural network. The proposed solution starts with a discretetime quasi-LPV system identification using artificial neural network. Subsequently, the robust controller is proposed, which does not take into account actuator saturation level and deals with the previously estimated faults. To check if the compensation problem is feasible, the robust invariant set is employed, which takes into account actuator saturation level. When the current state does not belong to the set, then a predictive control is performed in order to make such set larger. This makes it possible to increase the domain of attraction, which makes the proposed methodology an efficient solution for the fault-tolerant control. The last part of the paper presents an experimental results regarding wind turbines.

  13. The hypothalamic neural-glial network and the metabolic syndrome

    NARCIS (Netherlands)

    Jastroch, Martin; Morin, Silke; Tschöp, Matthias H.; Yi, Chun-Xia

    2014-01-01

    Despite numerous educational interventions and biomedical research efforts, modern society continues to suffer from obesity and its associated metabolic diseases, such as type 2 diabetes mellitus, and these diseases show little sign of abating. One reason for this is an incomplete understanding of

  14. Investigation of neural-net based control strategies for improved power system dynamic performance

    Energy Technology Data Exchange (ETDEWEB)

    Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  15. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  16. An Artificial Neural System for Autonomous Undersea Vehicles

    Science.gov (United States)

    1988-07-01

    Scale. Phylum Example Advance Ability Protozoa Paramecium No Nervous System Swim Food Discrimination Coelenterata Hydra Nerve Nets Spontaneity Anemone...Statolith Righting Jellyfish Escape Flatworms Planaria Bilateral Symmetry Kinesis Head Ganglion Taxis Commissures Conditioning Muhisensors Roundworms...to examine how nature has developed these capabilities. First, basic reflexes were established to orient the animal to critical features in its

  17. Power prediction in mobile communication systems using an optimal neural-network structure.

    Science.gov (United States)

    Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

    1997-01-01

    Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

  18. Bardoxolone methyl prevents obesity and hypothalamic dysfunction.

    Science.gov (United States)

    Camer, Danielle; Yu, Yinghua; Szabo, Alexander; Wang, Hongqin; Dinh, Chi H L; Huang, Xu-Feng

    2016-08-25

    High-fat (HF) diet-induced obesity is associated with hypothalamic leptin resistance and low grade chronic inflammation, which largely impairs the neuroregulation of negative energy balance. Neuroregulation of negative energy balance is largely controlled by the mediobasal and paraventricular nuclei regions of the hypothalamus via leptin signal transduction. Recently, a derivative of oleanolic acid, bardoxolone methyl (BM), has been shown to have anti-inflammatory effects. We tested the hypothesis that BM would prevent HF diet-induced obesity, hypothalamic leptin resistance, and inflammation in mice fed a HF diet. Oral administration of BM via drinking water (10 mg/kg daily) for 21 weeks significantly prevented an increase in body weight, energy intake, hyperleptinemia, and peripheral fat accumulation in mice fed a HF diet. Furthermore, BM treatment prevented HF diet-induced decreases in the anorexigenic effects of peripheral leptin administration. In the mediobasal and paraventricular nuclei regions of the hypothalamus, BM administration prevented HF diet-induced impairments of the downstream protein kinase b (Akt) pathway of hypothalamic leptin signalling. BM treatment also prevented an increase in inflammatory cytokines, tumour necrosis factor alpha (TNFα) and interleukin 6 (IL-6) in these two hypothalamic regions. These results identify a potential novel neuropharmacological application for BM in preventing HF diet-induced obesity, hypothalamic leptin resistance, and inflammation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  19. Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

    Science.gov (United States)

    Hardalaç, Firat

    2008-04-01

    Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

  20. Distributed Energy Neural Network Integration System: Year One Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Regan, T.; Sinnock, H.; Davis, A.

    2003-06-01

    This report describes the work of Orion Engineering Corp. to develop a DER household controller module and demonstrate the ability of a group of these controllers to operate through an intelligent, neighborhood controller. The controllers will provide a smart, technologically advanced, simple, efficient, and economic solution for aggregating a community of small distributed generators into a larger single, virtual generator capable of selling power or other services to a utility, independent system operator (ISO), or other entity in a coordinated manner.

  1. Internal models and neural computation in the vestibular system

    OpenAIRE

    Green, Andrea M.; Dora E. Angelaki

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem–...

  2. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-01-01

    Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

  3. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

    Energy Technology Data Exchange (ETDEWEB)

    Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

    2008-06-15

    Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

  4. 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.

  5. Hepatic branch vagus nerve plays a critical role in the recovery of post-ischemic glucose intolerance and mediates a neuroprotective effect by hypothalamic orexin-A.

    Directory of Open Access Journals (Sweden)

    Shinichi Harada

    Full Text Available Orexin-A (a neuropeptide in the hypothalamus plays an important role in many physiological functions, including the regulation of glucose metabolism. We have previously found that the development of post-ischemic glucose intolerance is one of the triggers of ischemic neuronal damage, which is suppressed by hypothalamic orexin-A. Other reports have shown that the communication system between brain and peripheral tissues through the autonomic nervous system (sympathetic, parasympathetic and vagus nerve is important for maintaining glucose and energy metabolism. The aim of this study was to determine the involvement of the hepatic vagus nerve on hypothalamic orexin-A-mediated suppression of post-ischemic glucose intolerance development and ischemic neuronal damage. Male ddY mice were subjected to middle cerebral artery occlusion (MCAO for 2 h. Intrahypothalamic orexin-A (5 pmol/mouse administration significantly suppressed the development of post-ischemic glucose intolerance and neuronal damage on day 1 and 3, respectively after MCAO. MCAO-induced decrease of hepatic insulin receptors and increase of hepatic gluconeogenic enzymes on day 1 after was reversed to control levels by orexin-A. This effect was reversed by intramedullary administration of the orexin-1 receptor antagonist, SB334867, or hepatic vagotomy. In the medulla oblongata, orexin-A induced the co-localization of cholin acetyltransferase (cholinergic neuronal marker used for the vagus nerve with orexin-1 receptor and c-Fos (activated neural cells marker. These results suggest that the hepatic branch vagus nerve projecting from the medulla oblongata plays an important role in the recovery of post-ischemic glucose intolerance and mediates a neuroprotective effect by hypothalamic orexin-A.

  6. Hepatic branch vagus nerve plays a critical role in the recovery of post-ischemic glucose intolerance and mediates a neuroprotective effect by hypothalamic orexin-A.

    Science.gov (United States)

    Harada, Shinichi; Yamazaki, Yui; Koda, Shuichi; Tokuyama, Shogo

    2014-01-01

    Orexin-A (a neuropeptide in the hypothalamus) plays an important role in many physiological functions, including the regulation of glucose metabolism. We have previously found that the development of post-ischemic glucose intolerance is one of the triggers of ischemic neuronal damage, which is suppressed by hypothalamic orexin-A. Other reports have shown that the communication system between brain and peripheral tissues through the autonomic nervous system (sympathetic, parasympathetic and vagus nerve) is important for maintaining glucose and energy metabolism. The aim of this study was to determine the involvement of the hepatic vagus nerve on hypothalamic orexin-A-mediated suppression of post-ischemic glucose intolerance development and ischemic neuronal damage. Male ddY mice were subjected to middle cerebral artery occlusion (MCAO) for 2 h. Intrahypothalamic orexin-A (5 pmol/mouse) administration significantly suppressed the development of post-ischemic glucose intolerance and neuronal damage on day 1 and 3, respectively after MCAO. MCAO-induced decrease of hepatic insulin receptors and increase of hepatic gluconeogenic enzymes on day 1 after was reversed to control levels by orexin-A. This effect was reversed by intramedullary administration of the orexin-1 receptor antagonist, SB334867, or hepatic vagotomy. In the medulla oblongata, orexin-A induced the co-localization of cholin acetyltransferase (cholinergic neuronal marker used for the vagus nerve) with orexin-1 receptor and c-Fos (activated neural cells marker). These results suggest that the hepatic branch vagus nerve projecting from the medulla oblongata plays an important role in the recovery of post-ischemic glucose intolerance and mediates a neuroprotective effect by hypothalamic orexin-A.

  7. Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response

    Energy Technology Data Exchange (ETDEWEB)

    Edelen, Auralee [Fermilab; Biedron, Sandra [Colorado State U., Fort Collins; Bowring, Daniel [Fermilab; Chase, Brian [Fermilab; Edelen, Jonathan [Fermilab; Milton, Stephen [Colorado State U., Fort Collins; Steimel, Jim [Fermilab

    2016-06-01

    As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.

  8. The NPY system and its neural and neuroendocrine regulation of bone.

    Science.gov (United States)

    Khor, Ee Cheng; Baldock, Paul

    2012-06-01

    The past decade has seen a significant expansion of our understanding of the interaction between the neural system and bone. While innervation of bone was long appreciated, the discovery of central relays from the hypothalamus to the cells of bone has seen the identification of a number of efferent neural pathways to bone. The neuropeptide Y (NPY) system has proven to represent a major central pathway, regulating the activity of osteoblasts and osteoclasts, through signaling of central and peripheral ligands, through specific receptors within the hypothalamus and the osteoblast. Moreover, this pathway is now recognized as acting to coordinate both skeletal and energy homeostasis. This review examines the mechanism and actions of the NPY pathway to regulate bone mass and bone cell activity.

  9. Immature visual neural system in children reflected by contrast sensitivity with adaptive optics correction

    Science.gov (United States)

    Liu, Rong; Zhou, Jiawei; Zhao, Haoxin; Dai, Yun; Zhang, Yudong; Tang, Yong; Zhou, Yifeng

    2014-01-01

    This study aimed to explore the neural development status of the visual system of children (around 8 years old) using contrast sensitivity. We achieved this by eliminating the influence of higher order aberrations (HOAs) with adaptive optics correction. We measured HOAs, modulation transfer functions (MTFs) and contrast sensitivity functions (CSFs) of six children and five adults with both corrected and uncorrected HOAs. We found that when HOAs were corrected, children and adults both showed improvements in MTF and CSF. However, the CSF of children was still lower than the adult level, indicating the difference in contrast sensitivity between groups cannot be explained by differences in optical factors. Further study showed that the difference between the groups also could not be explained by differences in non-visual factors. With these results we concluded that the neural systems underlying vision in children of around 8 years old are still immature in contrast sensitivity. PMID:24732728

  10. Based on Artificial Neural Network to Realize K-Parameter Analysis of Vehicle Air Spring System

    Science.gov (United States)

    Hung, San-Shan; Hsu, Chia-Ning; Hwang, Chang-Chou; Chen, Wen-Jan

    2017-10-01

    In recent years, because of the air-spring control technique is more mature, that air- spring suspension systems already can be used to replace the classical vehicle suspension system. Depend on internal pressure variation of the air-spring, thestiffnessand the damping factor can be adjusted. Because of air-spring has highly nonlinear characteristic, therefore it isn’t easy to construct the classical controller to control the air-spring effectively. The paper based on Artificial Neural Network to propose a feasible control strategy. By using offline way for the neural network design and learning to the air-spring in different initial pressures and different loads, offline method through, predict air-spring stiffness parameter to establish a model. Finally, through adjusting air-spring internal pressure to change the K-parameter of the air-spring, realize the well dynamic control performance of air-spring suspension.

  11. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  12. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  13. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  14. 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.

  15. Neural Systems Involved When Attending to a Speaker.

    Science.gov (United States)

    Kamourieh, Salwa; Braga, Rodrigo M; Leech, Robert; Newbould, Rexford D; Malhotra, Paresh; Wise, Richard J S

    2015-11-01

    Remembering what a speaker said depends on attention. During conversational speech, the emphasis is on working memory, but listening to a lecture encourages episodic memory encoding. With simultaneous interference from background speech, the need for auditory vigilance increases. We recreated these context-dependent demands on auditory attention in 2 ways. The first was to require participants to attend to one speaker in either the absence or presence of a distracting background speaker. The second was to alter the task demand, requiring either an immediate or delayed recall of the content of the attended speech. Across 2 fMRI studies, common activated regions associated with segregating attended from unattended speech were the right anterior insula and adjacent frontal operculum (aI/FOp), the left planum temporale, and the precuneus. In contrast, activity in a ventral right frontoparietal system was dependent on both the task demand and the presence of a competing speaker. Additional multivariate analyses identified other domain-general frontoparietal systems, where activity increased during attentive listening but was modulated little by the need for speech stream segregation in the presence of 2 speakers. These results make predictions about impairments in attentive listening in different communicative contexts following focal or diffuse brain pathology. © The Author 2015. Published by Oxford University Press.

  16. The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective

    Directory of Open Access Journals (Sweden)

    Joseph Billingsley

    2017-05-01

    Full Text Available Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model’s claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part.

  17. The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective.

    Science.gov (United States)

    Billingsley, Joseph; Losin, Elizabeth A R

    2017-01-01

    Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model's claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part.

  18. Calculation of transmission system losses for the Taiwan Power Company by the artificial neural network with time decayed weight

    Energy Technology Data Exchange (ETDEWEB)

    Chu, W.C.; Chen, B.K.; Mo, P.C. [Tatung Inst. of Tech., Taipei (Taiwan, Province of China)

    1995-12-31

    For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison will be made between the results of artificial neural network analysis and polynomial loss equations analysis.

  19. Use of neural networks in the capacitance imaging system. Technical note

    Energy Technology Data Exchange (ETDEWEB)

    Fasching, G.E.; Loudin, W.J.; Paton, D.E.; Smith, N.S. Jr.

    1993-10-01

    The US Department of Energy`s Morgantown Energy Technology Center (METC) has developed a capacitance imaging system (CIS) to support its fluidized-bed research programs. The CIS uses 400 electric displacement current measurements taken between combinations of pairs of 32 electrodes to obtain a measure of the fluidized-bed material density in the volume between the electrodes. The measurements are simultaneously made for three other sets of horizontally-oriented 32 electrodes with the four sets evenly spaced vertically. This report describes the development of a method of using the 400 current measurements per level as the input to a neural network to produce the 193-pixel density estimates defined for each level. A 417-neuron subnetwork using 4,047 weights is defined as the system used to determine a set of 32-pixel densities in one of the annular regions of the fluidized-bed cross section. The same subnetwork with different values of weights is used for the other five annular regions that cover the rest of the cross section. An averaging technique is used to determine the density of the small central region. The methods used to optimize the set of weights for each of the six subnetworks are described. The results of tests using calibration electric current data as inputs to the neural system showed that these density estimates have less error than three previously developed methods of converting current measurements into pixel density maps. A comparison of the density maps produced by the neural system and the alternate three methods using input fluidization data also indicates the superior performance of the neural network approach.

  20. Reconfigurable embedded system architecture for next-generation Neural Signal Processing.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2010-01-01

    This work presents a new architectural framework for next generation Neural Signal Processing (NSP). The essential features of the NSP hardware platform include scalability, reconfigurability, real-time processing ability and data storage. This proposed framework has been implemented in a proof-of-concept NSP prototype using an embedded system architecture synthesized in a Xilinx(®)Virtex(®)5 development board. The prototype includes a threshold-based spike detector and a fuzzy logic-based spike sorter.

  1. Functioning of Neural Systems Supporting Emotion Regulation in Anxiety-Prone Individuals

    OpenAIRE

    Campbell-Sills, Laura; Simmons, Alan N.; Lovero, Kathryn L.; Rochlin, Alexis A.; Martin P Paulus; Stein, Murray B.

    2010-01-01

    Previous neuroimaging studies suggest that prefrontal cortex (PFC) modulation of the amygdala and related limbic structures is an underlying neural substrate of effortful emotion regulation. Anxiety-prone individuals experience excessive negative emotions, signaling potential dysfunction of systems supporting down-regulation of negative emotions. We examined the hypothesis that anxious individuals require increased recruitment of lateral and medial PFC to decrease negative emotions. An emotio...

  2. Aplikasi Model Artificial Neural Network Terintegrasi dengan Geographycal Information System untuk Evaluasi Kesesuaian Lahan Perkebunan Kakao

    OpenAIRE

    Hermantoro; Rudiyanto; Slamet Suprayogi

    2008-01-01

    Land evaluation for specific purpose in plantation sector become very important due to increasing the competition in land use and the development of plantation sector. Land evaluation produces information of land economic values for specific land use. The objective of the research is to develop land evaluation method for cocoa estate using integrated model Artificial Neural Network (ANN) and Geographical Information System (GIS). Back propagation ANN model were used to predict cocoa yield bas...

  3. Placebo-Activated Neural Systems are Linked to Antidepressant Responses

    Science.gov (United States)

    Peciña, Marta; Bohnert, Amy S. B.; Sikora, Magdalena; Avery, Erich T.; Langenecker, Scott A.; Mickey, Brian J.; Zubieta, Jon-Kar

    2016-01-01

    Importance High placebo responses have been observed across a wide range of pathologies, severely impacting drug development. Objective Here we examined neurochemical mechanisms underlying the formation of placebo effects in patients with Major Depressive Disorder (MDD). Participants Thirty-five medication-free MDD patients. Design and Intervention We performed a single-blinded two-week cross-over randomized controlled trial of two identical oral placebos (described as having either “active” or “inactive” fast-acting antidepressant-like effects) followed by a 10-week open-label treatment with a selective serotonin reuptake inhibitor (SSRI) or in some cases, another agent as clinically indicated. The volunteers were studied with PET and the μ-opioid receptor (MOR)-selective radiotracer [11C]carfentanil after each 1-week “inactive” and “active” oral placebo treatment. In addition, 1 mL of isotonic saline was administered intravenously (i.v.) within sight of the volunteer during PET scanning every 4 min over 20 min only after the 1-week active placebo treatment, with instructions that the compound may be associated with the activation of brain systems involved in mood improvement. This challenge stimulus was utilized to test the individual capacity to acutely activate endogenous opioid neurotransmision under expectations of antidepressant effect. Setting A University Health System. Main Outcomes and Measures Changes in depressive symptoms in response to “active” placebo and antidepressant. Baseline and activation measures of MOR binding. Results Higher baseline MOR binding in the nucleus accumbens (NAc) was associated with better response to antidepressant treatment (r=0.48; p=0.02). Reductions in depressive symptoms after 1-week of “active” placebo treatment, compared to the “inactive”, were associated with increased placebo-induced μ-opioid neurotransmission in a network of regions implicated in emotion, stress regulation, and the

  4. Neural control of daily and seasonal timing of songbird migration.

    Science.gov (United States)

    Stevenson, Tyler J; Kumar, Vinod

    2017-07-01

    Bird migration is one of most salient annual events in nature. It involves predictable seasonal movements between breeding and non-breeding habitats. Both circadian and circannual clocks are entrained by photoperiodic cues and time daily and seasonal changes in migratory physiology and behavior. This mini-review provides an update on daily and seasonal rhythms of migratory behavior, and examines the neuroendocrine and molecular pathways involved in the timing of migration in songbirds. Recent findings have identified key neural substrates, and suggest the involvement of multiple neuroendocrine regulatory systems in controlling seasonal states in migrants. We propose that four distinct neural substrates are involved in the timing of migration and include (1) pineal gland and suprachiasmatic nucleus (mSCN); (2) a cluster of hypothalamic nuclei, the mediobasal hypothalamus (MBH); (3) dorsomedial hypothalamic nucleus (DMH); and (4) tanycytes along ependymal layer of the 3rd ventricle (3V). Cluster N, a nucleus in the telencephalon involved in the integration of geomagnetic cues, likely maintains functional connectivity with brain regions involved in timing songbird migration. These nuclei form an interconnected network that coordinates daily timing (pineal gland/mSCN), annual photoperiodic response (MBH, 3V), energetic state (MBH, DMH, 3V), and magnetic compass information (i.e., cluster N) for migration in songbirds.

  5. Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

    Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.

  6. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

    In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

  7. Altered Dynamics Between Neural Systems Sub-serving Decisions for Unhealthy Food

    Directory of Open Access Journals (Sweden)

    Qinghua eHe

    2014-11-01

    Full Text Available Using BOLD functional magnetic resonance imaging (fMRI techniques, we examined the relationships between activities in the neural systems elicited by the decision stage of the Iowa Gambling Task (IGT, and food choices of either vegetables or snacks high in fat and sugar. Twenty-three healthy normal weight adolescents and young adults, ranging in age from 14-21, were studied. Neural systems implicated in decision-making and inhibitory control were engaged by having participants perform the IGT during fMRI scanning. The Youth/Adolescent Questionnaire, a food frequency questionnaire, was used to obtain daily food choices. Higher consumption of vegetables correlated with higher activity in prefrontal cortical regions, namely the left superior frontal gyrus (SFG, and lower activity in sub-cortical regions, namely the right insular cortex. In contrast, higher consumption of fatty and sugary snacks correlated with lower activity in the prefrontal regions, combined with higher activity in the sub-cortical, insular cortex.These results provide preliminary support for our hypotheses that unhealthy food choices in real life are reflected by neuronal changes in key neural systems involved in habits, decision-making and self-control processes. These findings have implications for the creation of decision-making based intervention strategies that promote healthier eating.

  8. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    Science.gov (United States)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  9. Evolving a neural olfactorimotor system in virtual and real olfactory environments.

    Science.gov (United States)

    Rhodes, Paul A; Anderson, Todd O

    2012-01-01

    To provide a platform to enable the study of simulated olfactory circuitry in context, we have integrated a simulated neural olfactorimotor system with a virtual world which simulates both computational fluid dynamics as well as a robotic agent capable of exploring the simulated plumes. A number of the elements which we developed for this purpose have not, to our knowledge, been previously assembled into an integrated system, including: control of a simulated agent by a neural olfactorimotor system; continuous interaction between the simulated robot and the virtual plume; the inclusion of multiple distinct odorant plumes and background odor; the systematic use of artificial evolution driven by olfactorimotor performance (e.g., time to locate a plume source) to specify parameter values; the incorporation of the realities of an imperfect physical robot using a hybrid model where a physical robot encounters a simulated plume. We close by describing ongoing work toward engineering a high dimensional, reversible, low power electronic olfactory sensor which will allow olfactorimotor neural circuitry evolved in the virtual world to control an autonomous olfactory robot in the physical world. The platform described here is intended to better test theories of olfactory circuit function, as well as provide robust odor source localization in realistic environments.

  10. Neuropeptides, food intake and body weight regulation: a hypothalamic focus.

    Science.gov (United States)

    Hillebrand, J J G; de Wied, D; Adan, R A H

    2002-12-01

    Energy homeostasis is controlled by a complex neuroendocrine system consisting of peripheral signals like leptin and central signals, in particular, neuropeptides. Several neuropeptides with anorexigenic (POMC, CART, and CRH) as well as orexigenic (NPY, AgRP, and MCH) actions are involved in this complex (partly redundant) controlling system. Starvation as well as overfeeding lead to changes in expression levels of these neuropeptides, which act downstream of leptin, resulting in a physiological response. In this review the role of several anorexigenic and orexigenic (hypothalamic) neuropeptides on food intake and body weight regulation is summarized.

  11. Hypothalamic miRNAs: emerging roles in energy balance control.

    Science.gov (United States)

    Schneeberger, Marc; Gomez-Valadés, Alicia G; Ramirez, Sara; Gomis, Ramon; Claret, Marc

    2015-01-01

    The hypothalamus is a crucial central nervous system area controlling appetite, body weight and metabolism. It consists in multiple neuronal types that sense, integrate and generate appropriate responses to hormonal and nutritional signals partly by fine-tuning the expression of specific batteries of genes. However, the mechanisms regulating these neuronal gene programmes in physiology and pathophysiology are not completely understood. MicroRNAs (miRNAs) are key regulators of gene expression that recently emerged as pivotal modulators of systemic metabolism. In this article we will review current evidence indicating that miRNAs in hypothalamic neurons are also implicated in appetite and whole-body energy balance control.

  12. Hypothalamic miRNAs: emerging roles in energy balance control

    Directory of Open Access Journals (Sweden)

    Marc eSchneeberger

    2015-02-01

    Full Text Available The hypothalamus is a crucial central nervous system area controlling appetite, body weight and metabolism. It consists in multiple neuronal types that sense, integrate and generate appropriate responses to hormonal and nutritional signals partly by fine-tuning the expression of specific batteries of genes. However, the mechanisms regulating these neuronal gene programmes in physiology and pathophysiology are not completely understood. MicroRNAs (miRNAs are key regulators of gene expression that recently emerged as pivotal modulators of systemic metabolism. In this article we will review current evidence indicating that miRNAs in hypothalamic neurons are also implicated in appetite and whole-body energy balance control.

  13. Modelling prenatal bacterial infection: functional consequences of altered hypothalamic pituitary adrenal axis development.

    Science.gov (United States)

    Hodyl, Nicolette A; Walker, Frederick R; Krivanek, Klara M; Clifton, Vicki; Hodgson, Deborah M

    2007-03-12

    Prenatal exposure of animals to bacterial endotoxin is an experimental model of systemic maternal infection in the human pregnancy. Previous studies in the rat have demonstrated that such exposure is associated with long term alterations to hypothalamic pituitary adrenal (HPA) axis development. Typically, these animals display an elevated HPA response to stress in adulthood. As neural development is more similar in the human and the guinea pig than the rat, this study adopted a guinea pig model of pregnancy to explore the effects of endotoxin exposure on the HPA axis in the offspring. The offspring of dams exposed to endotoxin exhibited an attenuated cortisol response to the novel environment stress in the weaning period. The degree to which this cortisol response was both buffered by the mother's presence, and habituated to on repeated exposure, differed significantly between the prenatal treatment groups. In adulthood, a diminished cortisol response to the immune challenge was only evident in the female offspring, while both male and female offspring exhibited altered febrile responses. The results of the present study indicate that prenatal bacterial exposure in the guinea pig results in offspring with lower cortisol responses to stress in later life. These findings contrast past research that has used the rat to model pregnancy. As such, the use of the guinea pig to model infection may provide a useful alternative model of human pregnancy to explore programming effects.

  14. TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

    DEFF Research Database (Denmark)

    Yao, Wei; Fang, Jiakun; Zhao, Ping

    2013-01-01

    In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency...

  15. Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

    Directory of Open Access Journals (Sweden)

    Reyes-Reyes J.

    2000-01-01

    Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

  16. Vestibular system and neural correlates of motion sickness

    Science.gov (United States)

    Miller, Alan D.

    1986-01-01

    Initial studies re-examine the role of certain central nervous system structures in the production of vestibular-induced vomiting and vomiting in general. All experiments were conducted using cats. Since these studies demonstrated that the essential role of various central structures in vestibular-induced vomiting is only poorly understood, efforts were re-directed to study the control of the effector muscles (diaphragm and abdominal muscles) that produce the pressure changes responsible for vomiting, with the goal of determining how this control mechanism is engaged during motion sickness. Experiments were conducted to localize the motoneurons that innervate the individual abdominal muscles and the portion of the diaphragm that surrounds the esophagus. A central question regarding respiratory muscle control during vomiting is whether these muscles are activated via the same brain stem pre-motor neurons that provide descending respiratory drive and/or by other descending input(s). In other experiments, the use of a combination of pitch and roll motions to produce motion sickness in unrestrained cats was evaluated. This stimulus combination can produce vomiting in only the most susceptible cats and is thus not as provacative a stimulus for cats as vertical linear acceleration.

  17. A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System

    Directory of Open Access Journals (Sweden)

    Xiaohu Li

    2013-01-01

    Full Text Available Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI and proportional integral differentiation (PID cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.

  18. Developmental changes in hypothalamic oxytocin and oxytocin receptor mRNA expression and their sensitivity to fasting in male and female rats.

    Science.gov (United States)

    Matsuzaki, Toshiya; Iwasa, Takeshi; Munkhzaya, Munkhsaikhan; Tungalagsuvd, Altankhuu; Kawami, Takako; Murakami, Masahiro; Yamasaki, Mikio; Yamamoto, Yuri; Kato, Takeshi; Kuwahara, Akira; Yasui, Toshiyuki; Irahara, Minoru

    2015-04-01

    Oxytocin (OT) affects the central nervous system and is involved in a variety of social and non-social behaviors. Recently, the role played by OT in energy metabolism and its organizational effects on estrogen receptor alpha (ER-α) during the neonatal period have gained attention. In this study, the developmental changes in the hypothalamic mRNA levels of OT, the OT receptor (OTR), and ER-α were evaluated in male and female rats. In addition, the fasting-induced changes in the hypothalamic mRNA levels of OT and the OTR were evaluated. Hypothalamic explants were taken from postnatal day (PND) 10, 20, and 30 rats, and the mRNA level of each molecule was measured. Hypothalamic OT mRNA expression increased throughout the developmental period in both sexes. The rats' hypothalamic OTR mRNA levels were highest on PND 10 and decreased throughout the developmental period. In the male rats, the hypothalamic mRNA levels of ER-α were higher on PND 30 than on PND 10. On the other hand, no significant differences in hypothalamic ER-α mRNA expression were detected among the examined time points in the female rats, although hypothalamic ER-α mRNA expression tended to be higher on PND 30 than on PND 10. Significant positive correlations were detected between hypothalamic OT and ER-α mRNA expression in both the male and female rats. Hypothalamic OT mRNA expression was not affected by fasting at any of the examined time points in either sex. These results indicate that hypothalamic OT expression is not sensitive to fasting during the developmental period. In addition, as a positive correlation was detected between hypothalamic OT and ER-α mRNA expression, these two molecules might interact with each other to induce appropriate neuronal development. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  20. Stochastic resonance in FitzHugh-Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise

    Science.gov (United States)

    Guo, Yong-Feng; Xi, Bei; Wei, Fang; Tan, Jian-Guo

    2017-12-01

    In this paper, the phenomenon of stochastic resonance in FitzHugh-Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

  1. Cerebral activations during viewing of food stimuli in adult patients with acquired structural hypothalamic damage: a functional neuroimaging study.

    Science.gov (United States)

    Steele, C A; Powell, J L; Kemp, G J; Halford, J C G; Wilding, J P; Harrold, J A; Kumar, S V D; Cuthbertson, D J; Cross, A A; Javadpour, M; MacFarlane, I A; Stancak, A A; Daousi, C

    2015-09-01

    Obesity is common following hypothalamic damage due to tumours. Homeostatic and non-homeostatic brain centres control appetite and energy balance but their interaction in the presence of hypothalamic damage remains unknown. We hypothesized that abnormal appetite in obese patients with hypothalamic damage results from aberrant brain processing of food stimuli. We sought to establish differences in activation of brain food motivation and reward neurocircuitry in patients with hypothalamic obesity (HO) compared with patients with hypothalamic damage whose weight had remained stable. In a cross-sectional study at a University Clinical Research Centre, we studied 9 patients with HO, 10 age-matched obese controls, 7 patients who remained weight-stable following hypothalamic insult (HWS) and 10 non-obese controls. Functional magnetic resonance imaging was performed in the fasted state, 1 h and 3 h after a test meal, while subjects were presented with images of high-calorie foods, low-calorie foods and non-food objects. Insulin, glucagon-like peptide-1, Peptide YY and ghrelin were measured throughout the experiment, and appetite ratings were recorded. Mean neural activation in the posterior insula and lingual gyrus (brain areas linked to food motivation and reward value of food) in HWS were significantly lower than in the other three groups (P=0.001). A significant negative correlation was found between insulin levels and posterior insula activation (P=0.002). Neural pathways associated with food motivation and reward-related behaviour, and the influence of insulin on their activation may be involved in the pathophysiology of HO.

  2. Efferent connections from the lateral hypothalamic region and the lateral preoptic area to the hypothalamic paraventricular nucleus of the rat

    DEFF Research Database (Denmark)

    Larsen, P J; Hay-Schmidt, Anders; Mikkelsen, J D

    1994-01-01

    , iontophoretic injections of the anterograde tracer Phaseolus vulgaris-leucoagglutinin were delivered into distinct areas of the lateral hypothalamic region. Neurons of the intermediate hypothalamic area projected mainly to the PVN subnuclei, which contained parvicellular neuroendocrine cells. In contrast...

  3. Social cognitive conflict resolution: Contributions of domain general and domain specific neural systems

    Science.gov (United States)

    Zaki, Jamil; Hennigan, Kelly; Weber, Jochen; Ochsner, Kevin N.

    2010-01-01

    Cognitive control mechanisms allow individuals to behave adaptively in the face of complex and sometimes conflicting information. While the neural bases of these control mechanisms have been examined in many contexts, almost no attention has been paid to their role in resolving conflicts between competing social cues, which is surprising, given that cognitive conflicts are part of many social interactions. Evidence about the neural processing of social information suggests that two systems—the mirror neuron system (MNS) and mental state attribution system (MSAS)—are specialized for processing nonverbal and contextual social cues, respectively. This could support a model of social cognitive conflict resolution in which competition between social cues would recruit domain-general cognitive control mechanisms, which in turn would bias processing towards the MNS or MSAS. Such biasing could also alter social behaviors, such as inferences made about the internal states of others. We tested this model by scanning participants using fMRI while they drew inferences about social targets' emotional states based on congruent or incongruent nonverbal and contextual social cues. Conflicts between social cues recruited the anterior cingulate and lateral prefrontal cortex, brain areas associated with domain-general control processes. This activation was accompanied by biasing of neural activity towards areas in the MNS or MSAS, which tracked, respectively, with perceivers' behavioral reliance on nonverbal or contextual cues when drawing inferences about targets' emotions. Together, these data provide evidence about both domain general and domain specific mechanisms involved in resolving social cognitive conflicts. PMID:20573895

  4. Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

    Directory of Open Access Journals (Sweden)

    Veronica Chan

    2017-03-01

    Full Text Available This paper presents the application of a neural network rule extraction algorithm, called the piece-wise linear artificial neural network or PWL-ANN algorithm, on a carbon capture process system dataset. The objective of the application is to enhance understanding of the intricate relationships among the key process parameters. The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN. The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach, in which accuracies of the generated predictive models are often not satisfactory, and the opaqueness of the ANN models. The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system. An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO2 production rate are the steam flow rate through reboiler, reboiler pressure, and the CO2 concentration in the flue gas.

  5. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  6. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  7. NONLINEAR SYSTEM MODELING USING SINGLE NEURON CASCADED NEURAL NETWORK FOR REAL-TIME APPLICATIONS

    Directory of Open Access Journals (Sweden)

    S. Himavathi

    2012-04-01

    Full Text Available Neural Networks (NN have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationally less complex. In this paper a parametric method of analysis is adopted to determine the compact and faster NN model among various neural network architectures. This work proves through analysis and examples that the Single Neuron Cascaded (SNC architecture is distinct in providing compact and simpler models requiring lower execution time. The unique structural growth of SNC architecture enables automation in design. The SNC Network is shown to combine the advantages of both single and multilayer neural network architectures. Extensive analysis on selected architectures and their models for four benchmark nonlinear theoretical plants and a practical application are tested. A performance comparison of the NN models is presented to demonstrate the superiority of the single neuron cascaded architecture for online real time applications.

  8. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

    Science.gov (United States)

    Kang, Min-Joo

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802

  9. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Huang, K. S.; Diep, J.

    1993-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  10. Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

    Science.gov (United States)

    Olyaee, Saeed; Hamedi, Samaneh

    2011-02-01

    In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

  11. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Science.gov (United States)

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

  12. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Directory of Open Access Journals (Sweden)

    Min-Joo Kang

    Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

  13. Hypothalamic Non-AgRP, Non-POMC GABAergic Neurons Are Required for Postweaning Feeding and NPY Hyperphagia.

    Science.gov (United States)

    Kim, Eun Ran; Wu, Zhaofei; Sun, Hao; Xu, Yuanzhong; Mangieri, Leandra R; Xu, Yong; Tong, Qingchun

    2015-07-22

    The hypothalamus is critical for feeding and body weight regulation. Prevailing studies focus on hypothalamic neurons that are defined by selectively expressing transcription factors or neuropeptides including those expressing proopiomelanocortin (POMC) and agouti-related peptides (AgRP). The Cre expression driven by the pancreas-duodenum homeobox 1 promoter is abundant in several hypothalamic nuclei but not in AgRP or POMC neurons. Using this line, we generated mice with disruption of GABA release from a major subset of non-POMC, non-AgRP GABAergic neurons in the hypothalamus. These mice exhibited a reduction in postweaning feeding and growth, and disrupted hyperphagic responses to NPY. Disruption of GABA release severely diminished GABAergic input to the paraventricular hypothalamic nucleus (PVH). Furthermore, disruption of GABA-A receptor function in the PVH also reduced postweaning feeding and blunted NPY-induced hyperphagia. Given the limited knowledge on postweaning feeding, our results are significant in identifying GABA release from a major subset of less appreciated hypothalamic neurons as a key mediator for postweaning feeding and NPY hyperphagia, and the PVH as one major downstream site that contributes significantly to the GABA action. Significance statement: Prevalent studies on feeding in the hypothalamus focus on well characterized, selective groups neurons [e.g., proopiomelanocortin (POMC) and agouti-related peptide (AgRP) neurons], and as a result, the role of the majority of other hypothalamic neurons is largely neglected. Here, we demonstrated an important role for GABAergic projections from non-POMC non-AgRP neurons to the paraventricular hypothalamic nucleus in promoting postweaning (mainly nocturnal) feeding and mediating NPY-induced hyperphagia. Thus, these results signify an importance to study those yet to be defined hypothalamic neurons in the regulation of energy balance and reveal a neural basis for postweaning (nocturnal) feeding and

  14. Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

    Science.gov (United States)

    Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

    2013-01-01

    The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy

  15. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  16. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

    Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

  17. Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia.

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Friehs, Gerhard M; Black, Michael J

    2011-04-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (one participant). This study suggests that signals from a small ensemble of motor cortical neurons (∼40) can be used for natural point-and-click 2-D cursor control of a personal computer.

  18. Convergence of Asymptotic Systems of Non-autonomous Neural Network Models with Infinite Distributed Delays

    Science.gov (United States)

    Oliveira, José J.

    2017-10-01

    In this paper, we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.

  19. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

    Directory of Open Access Journals (Sweden)

    Ibnkahla Mohamed

    2003-01-01

    Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

  20. Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

    Science.gov (United States)

    Mclauchlan, Robert A.

    1989-01-01

    Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

  1. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  2. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available 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.

  3. Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.

    Science.gov (United States)

    Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M

    2011-08-01

    During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

  4. Changes in the responsiveness of hypothalamic prokineticin 2 mRNA expression to food deprivation in developing female rats.

    Science.gov (United States)

    Iwasa, Takeshi; Matsuzaki, Toshiya; Munkhzaya, Munkhsaikhan; Tungalagsuvd, Altankhuu; Kawami, Takako; Murakami, Masahiro; Yamasaki, Mikio; Kato, Takeshi; Kuwahara, Akira; Yasui, Toshiyuki; Irahara, Minoru

    2014-05-01

    Prokineticin 2 (PK2) is highly expressed in several regions of the central nervous system, including the hypothalamus. Recently, it has been suggested that PK2 plays a role in appetite regulation. In adult male rodents, the administration of PK2 decreased food intake, and PK2 mRNA expression was reduced by food deprivation. Usually, the changes in the expression levels of appetite-regulating factors induced in response to fasting are not fully established during the neonatal period. Thus, we investigated the developmental changes in hypothalamic PK2 mRNA expression and the alterations in hypothalamic PK2 mRNA expression induced by fasting during the pre-pubertal period in female rats. The changes in hypothalamic neuropeptide Y (NPY) mRNA expression were also examined because NPY is a potent appetite regulatory factor. Hypothalamic PK2 mRNA expression was extremely high during the early neonatal period (postnatal day (PND) 5) compared with that observed during subsequent periods (PND15, 25, and 42), while hypothalamic NPY mRNA expression did not differ among any of the examined periods. A fasting-induced reduction in hypothalamic PK2 mRNA expression was observed on PND5, but no fasting-induced increase in hypothalamic NPY mRNA expression was seen during the same period. In addition, the fasting-induced reduction in hypothalamic PK2 mRNA expression observed on PND5 was more marked than that seen on PND25. These results suggest that the sensitivity of hypothalamic PK2 expression to undernutrition develops during the early neonatal period, when the responses of other appetite regulatory factors to such pressures remain immature. Copyright © 2014 ISDN. Published by Elsevier Ltd. All rights reserved.

  5. Cognitive brain responses during circadian wake-promotion: evidence for sleep-pressure-dependent hypothalamic activations.

    Science.gov (United States)

    Reichert, Carolin F; Maire, Micheline; Gabel, Virginie; Viola, Antoine U; Götz, Thomas; Scheffler, Klaus; Klarhöfer, Markus; Berthomier, Christian; Strobel, Werner; Phillips, Christophe; Salmon, Eric; Cajochen, Christian; Schmidt, Christina

    2017-07-17

    The two-process model of sleep-wake regulation posits that sleep-wake-dependent homeostatic processes interact with the circadian timing system to affect human behavior. The circadian timing system is fundamental to maintaining stable cognitive performance, as it counteracts growing homeostatic sleep pressure during daytime. Using magnetic resonance imaging, we explored brain responses underlying working memory performance during the time of maximal circadian wake-promotion under varying sleep pressure conditions. Circadian wake-promoting strength was derived from the ability to sleep during an evening nap. Hypothalamic BOLD activity was positively linked to circadian wake-promoting strength under normal, but not under disproportionally high or low sleep pressure levels. Furthermore, higher hypothalamic activity under normal sleep pressure levels predicted better performance under sleep loss. Our results reappraise the two-process model by revealing a homeostatic-dose-dependent association between circadian wake-promotion and cognition-related hypothalamic activity.

  6. Foetal Hypothalamic and Pituitary Expression of Gonadotrophin Releasing Hormone and Galanin Systems is Disturbed by Exposure to Sewage Sludge Chemicals via Maternal Ingestion

    Science.gov (United States)

    Bellingham, Michelle; Fowler, Paul A.; Amezaga, Maria R.; Whitelaw, Christine M.; Rhind, Stewart M.; Cotinot, Corinne; Mandon-Pepin, Beatrice; Sharpe, Richard M.; Evans, Neil P.

    2016-01-01

    Animals and humans are chronically exposed to endocrine disrupting chemicals (EDCs) which are ubiquitous in the environment. There are strong circumstantial links between environmental EDC exposure and both declining human/wildlife reproductive health and the increasing incidence of reproductive system abnormalities. Verification of such links, however, is difficult and requires animal models exposed to 'real life', environmentally relevant concentrations/mixtures of environmental contaminants (ECs), particularly in- utero, when sensitivity to EC exposure is high. The aim of this study was to determine whether the fetal sheep reproductive neuroendocrine axis, particularly GnRH and galaninergic systems were affected by maternal exposure to a complex mixture of chemicals, applied to pasture, in the form of sewage sludge. Sewage sludge contains high concentrations of a spectrum of EDCs and other pollutants, relative to environmental concentrations but is frequently recycled to land as a fertiliser. We found that foetuses exposed, to the EDC mixture in-utero through their mothers, had lower GnRH mRNA expression in the hypothalamus and lower GnRHR and galanin receptor (GALR) mRNA expression in the hypothalamus and pituitary gland. Strikingly, this, treatment had no significant effect on maternal GnRH or GnRHR mRNA expression although GALR mRNA expression within the maternal hypothalamus and pituitary gland was reduced. This study clearly demonstrates that the developing foetal neuroendocrine axis is sensitive to real-world mixtures of environmental chemicals. Given the important role of GnRH and GnRHR in the regulation of reproductive function, its known in-utero programming role, and the role of galanin in the regulation of many physiological/neuroendocrine systems, in-utero changes in the activity of these systems are likely to have long term consequences in adulthood and represent a novel pathway through which EC mixtures could perturb normal reproductive function

  7. Foetal Hypothalamic and Pituitary Expression of Gonadotrophin Releasing Hormone and Galanin Systems is Disturbed by Exposure to Sewage Sludge Chemicals via Maternal Ingestion

    OpenAIRE

    Bellingham, Michelle; Fowler, Paul A; Amezaga, Maria R.; Whitelaw, Christine M.; Rhind, Stewart M; Cotinot, Corinne; Mandon-Pepin, Beatrice; Richard M Sharpe; Evans, Neil P.

    2010-01-01

    Animals and humans are chronically exposed to endocrine disrupting chemicals (EDCs) which are ubiquitous in the environment. There are strong circumstantial links between environmental EDC exposure and both declining human/wildlife reproductive health and the increasing incidence of reproductive system abnormalities. Verification of such links, however, is difficult and requires animal models exposed to 'real life', environmentally relevant concentrations/mixtures of environmental contaminant...

  8. Foetal hypothalamic and pituitary expression of gonadotrophin-releasing hormone and galanin systems is disturbed by exposure to sewage sludge chemicals via maternal ingestion.

    Science.gov (United States)

    Bellingham, M; Fowler, P A; Amezaga, M R; Whitelaw, C M; Rhind, S M; Cotinot, C; Mandon-Pepin, B; Sharpe, R M; Evans, N P

    2010-06-01

    Animals and humans are chronically exposed to endocrine disrupting chemicals (EDCs) that are ubiquitous in the environment. There are strong circumstantial links between environmental EDC exposure and both declining human/wildlife reproductive health and the increasing incidence of reproductive system abnormalities. The verification of such links, however, is difficult and requires animal models exposed to 'real life', environmentally relevant concentrations/mixtures of environmental contaminants (ECs), particularly in utero, when sensitivity to EC exposure is high. The present study aimed to determine whether the foetal sheep reproductive neuroendocrine axis, particularly gondotrophin-releasing hormone (GnRH) and galaninergic systems, were affected by maternal exposure to a complex mixture of chemicals, applied to pasture, in the form of sewage sludge. Sewage sludge contains high concentrations of a spectrum of EDCs and other pollutants, relative to environmental concentrations, but is frequently recycled to land as a fertiliser. We found that foetuses exposed to the EDC mixture in utero through their mothers had lower GnRH mRNA expression in the hypothalamus and lower GnRH receptor (GnRHR) and galanin receptor (GALR) mRNA expression in the hypothalamus and pituitary gland. Strikingly, this, treatment had no significant effect on maternal GnRH or GnRHR mRNA expression, although GALR mRNA expression within the maternal hypothalamus and pituitary gland was reduced. The present study clearly demonstrates that the developing foetal neuroendocrine axis is sensitive to real-world mixtures of environmental chemicals. Given the important role of GnRH and GnRHR in the regulation of reproductive function, its known role programming role in utero, and the role of galanin in the regulation of many physiological/neuroendocrine systems, in utero changes in the activity of these systems are likely to have long-term consequences in adulthood and represent a novel pathway through

  9. A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.

    Science.gov (United States)

    Bommanna Raja, K; Madheswaran, M; Thyagarajah, K

    2008-02-01

    The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

  10. Role of developmental factors in hypothalamic function

    Directory of Open Access Journals (Sweden)

    Jakob eBiran

    2015-04-01

    Full Text Available The hypothalamus is a brain region which regulates homeostasis by mediating endocrine, autonomic and behavioral functions. It is comprised of several nuclei containing distinct neuronal populations producing neuropeptides and neurotransmitters that regulate fundamental body functions including temperature and metabolic rate, thirst and hunger, sexual behavior and reproduction, circadian rhythm, and emotional responses. The identity, number and connectivity of these neuronal populations are established during the organism’s development and are of crucial importance for normal hypothalamic function. Studies have suggested that developmental abnormalities in specific hypothalamic circuits can lead to obesity, sleep disorders, anxiety, depression and autism. At the molecular level, the development of the hypothalamus is regulated by transcription factors, secreted growth factors, neuropeptides and their receptors. Recent studies in zebrafish and mouse have demonstrated that some of these molecules maintain their expression in the adult brain and subsequently play a role in the physiological functions that are regulated by hypothalamic neurons. Here, we summarize the involvement of some of the key developmental factors in hypothalamic development and function by focusing on the mouse and zebrafish genetic model organisms.

  11. Evolution of Gelastic Epilepsy with Hypothalamic Hamartoma

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-11-01

    Full Text Available The patterns of clinical presentation, evolution of the epilepsy, and electoclinical diagnostic features of hypothalamic hamartoma (HH in 19 patients (8 children and 11 adults, seen between 1991 and 2001, were evaluated at Kings College Hospital and the Institute of Epileptology, London, UK.

  12. Hypothalamic functions in patients with pituitary insufficiency

    NARCIS (Netherlands)

    Borgers, A.J.F.

    2013-01-01

    The main objective of this thesis is to increase our understanding of hypothalamic (dys)function in patients with pituitary insufficiency. This goal is driven by the clinical experience of persisting symptoms in patients adequately treated for pituitary insufficiency. We focus primarily on patients

  13. Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome may have a hypothalamus-periaqueductal gray localization.

    Science.gov (United States)

    Chow, Cristelle; Fortier, Marielle Valerie; Das, Lena; Menon, Anuradha P; Vasanwala, Rashida; Lam, Joyce C M; Ng, Zhi Min; Ling, Simon Robert; Chan, Derrick W S; Choong, Chew Thye; Liew, Wendy K M; Thomas, Terrence

    2015-05-01

    Anatomical localization of the rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome has proved elusive. Most patients had neuroimaging after cardiorespiratory collapse, revealing a range of ischemic lesions. A 15-year-old obese boy with an acute febrile encephalopathy had hypoventilation, autonomic dysfunction, visual hallucinations, hyperekplexia, and disordered body temperature, and saltwater regulation. These features describe the ROHHAD syndrome. Cerebrospinal fluid analysis showed pleocytosis, elevated neopterins, and oligoclonal bands, and serology for systemic and antineuronal antibodies was negative. He improved after receiving intravenous steroids, immunoglobulins, and long-term mycophenolate. Screening for neural crest tumors was negative. Magnetic resonance imaging of the brain early in his illness showed focal inflammation in the periaqueductal gray matter and hypothalamus. This unique localization explains almost all symptoms of this rare autoimmune encephalitis. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2017-01-01

    Full Text Available Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

  15. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  16. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

    Science.gov (United States)

    Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

  17. Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

    Science.gov (United States)

    Lima, Pedro; Beard, Randal

    1992-01-01

    The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a

  18. Hypothalamic neurosecretory and circadian vasopressinergic neuronal systems in the blind cone-rod homeobox knock out mouse (Crx(-/-) ) and the 129sv wild type mouse

    DEFF Research Database (Denmark)

    Rovsing, Louise; Rath, Martin Fredensborg; Møller, Morten

    2013-01-01

    Vasopressin (AVP) is both a neuroendocrine hormone located in magnocellular neurosecretory neurons of the hypothalamus of mammals but also a neurotransmitter/neuromodulator in the parvocellular suprachiasmatic nucleus (SCN). The SCN is the endogenous clock of the brain and exhibits a prominent...... circadian AVP-rhythm. We have in this study of the brown 129sv mouse and the visual blind cone-rod homeobox gene knock out mouse (Crx(-/-) ) with degeneration of the retinal rods and cones, but a preserved non-image forming optic system, studied the temporal Avp-expression in both the neurosecretory...

  19. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Md. Moqbul Hossain

    2013-01-01

    Full Text Available Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH, organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN to train and validate the data derived from Geographic Information System (GIS spatial distribution grids. The neural network architecture with (6-20-1 pattern was able to predict the arsenic concentration with reasonable accuracy.

  1. Neural synchrony within the motor system: what have we learned so far?

    Directory of Open Access Journals (Sweden)

    Bernadette C. M. van Wijk

    2012-09-01

    Full Text Available Synchronization of neural activity is considered essential for information processing in the nervous system. Both local and inter-regional synchronization are omnipresent in different frequency regimes and relate to a variety of behavioral and cognitive functions. Over the years, many studies have sought to elucidate the question how alpha/mu, beta, and gamma synchronization contribute to motor control. Here, we review these studies with the purpose to delineate what they have added to our understanding of the neural control of movement. We highlight important findings regarding oscillations in primary motor cortex, synchronization between cortex and spinal cord, synchronization between cortical regions, as well as abnormal synchronization patterns in a selection of motor dysfunctions. The interpretation of synchronization patterns benefits from combining results of invasive and non-invasive recordings, different data analysis tools, and modeling work. Importantly, although synchronization is deemed to play a vital role, it is not the only mechanism for neural communication. Spike timing and rate coding act together during motor control and should therefore both be accounted for when interpreting movement-related activity.

  2. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  3. Design a PID Controller for Suspension System by Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    M. Heidari

    2013-01-01

    Full Text Available This paper presents a neural network for designing of a PID controller for suspension system. The suspension system, designed as a quarter model, is used to simplify the problem to one-dimensional spring-damper system. In this paper, back propagation neural network (BPN has been used for determining the gain parameters of a PID controller for suspension system of automotive. The BPN method is found to be the most accurate and quick. The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. Training was continued until the mean squared error is less than . Desired error value was achieved in the BPN, and the BPN was tested with both data used and not used for training. By training of this network, it is possible to estimate the gain parameters of PID controller at any condition. The inputs of network are automotive velocity, overshoot percentage, settling time, and steady state error of suspension system response. Also outputs of the net are the gain parameters of PID controller. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area.

  4. Análise crítica dos sistemas neurais envolvidos nas respostas de medo inato Critical analysis of the neural systems organizing innate fear responses

    Directory of Open Access Journals (Sweden)

    Newton Sabino Canteras

    2003-12-01

    (typically freezing, it is not known if the neural systems underlying an array of defensive responses to innate, unconditioned, painless threat stimuli, and conditioning to these stimuli, are the same as those involved in foot shock and its conditioning sequellae. Recent work involving lesions and c-Fos activation in conjunction with predator or predator odor exposure suggest specific neural systems for response to these, potentially different from the systems outlined in Pavlovian fear conditioning studies. As outlined in the present review, these systems include the medial hypothalamic defensive circuit; specific amygdalar and septo-hippocampal territories, involved in processing, respectively, cues related to the predator presence and environmental contextual analysis; and the periaqueductal gray, known to be critically involved in the expression of predator-induced responses. This information may be potentially important in analysis of defense-related psychopathologies and in the design of therapeutic interventions for them.

  5. The cerebellum: A neural system for the study of reinforcement learning

    Directory of Open Access Journals (Sweden)

    Rodney A. Swain

    2011-03-01

    Full Text Available In its strictest application, the term reinforcement learning refers to a computational approach to learning in which an agent (often a machine interacts with a mutable environment to maximize reward through trial and error. The approach borrows essentials from several fields, most notably Computer Science, Behavioral Neuroscience, and Psychology. At the most basic level, a neural system capable of mediating reinforcement learning must be able to acquire sensory information about the external environment and internal milieu (either directly or through connectivities with other brain regions, must be able to select a behavior to be executed, and must be capable of providing evaluative feedback about the success of that behavior. Given that Psychology informs us that reinforcers, both positive and negative, are stimuli or consequences that increase the probability that the immediately antecedent behavior will be repeated and that reinforcer strength or viability is modulated by the organism’s past experience with the reinforcer, its affect, and even the state of its muscles (e.g., eyes open or closed; it is the case that any neural system that supports reinforcement learning must also be sensitive to these same considerations. Once learning is established, such a neural system must finally be able to maintain continued response expression and prevent response drift. In this report, we examine both historical and recent evidence that the cerebellum satisfies all of these requirements. While we report evidence from a variety of learning paradigms, the majority of our discussion will focus on classical conditioning of the rabbit eye blink response as an ideal model system for the study of reinforcement and reinforcement learning.

  6. A new approach for sizing stand alone photovoltaic systems based in neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hontoria, L.; Aguilera, J. [Universidad de Jaen, Dept. de Electronica, Jaen (Spain); Zufiria, P. [UPM Ciudad Universitaria, Dept. de Matematica Aplicada a las Tecnologias de la Informacion, Madrid (Spain)

    2005-02-01

    Several methods for sizing stand alone photovoltaic (pv) systems has been developed. The more simplistic are called intuitive methods. They are a useful tool for a first approach in sizing stand alone photovoltaic systems. Nevertheless they are very inaccurate. Analytical methods use equations to describe the pv system size as a function of reliability. These ones are more accurate than the previous ones but they are also not accurate enough for sizing of high reliability. In a third group there are methods which use system simulations. These ones are called numerical methods. Many of the analytical methods employ the concept of reliability of the system or the complementary term: loss of load probability (LOLP). In this paper an improvement for obtaining LOLP curves based on the neural network called Multilayer Perceptron (MLP) is presented. A unique MLP for many locations of Spain has been trained and after the training, the MLP is able to generate LOLP curves for any value and location. (Author)

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

    Science.gov (United States)

    Lenzi, Delia; Trentini, Cristina; Tambelli, Renata; Pantano, Patrizia

    2015-01-01

    The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional, and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the “attachment-caregiving social bond” (Bowlby, 1969). We then follow with those neuroimaging studies that have focused on this particular issue, i.e., those which have studied the activation of the careging system in women (using infant stimuli) and have explored how the individual attachment model (through the Adult Attachment Interview) modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction. PMID:26379578

  8. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    Science.gov (United States)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

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

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

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

  10. Reactive Power based Model Reference Neural Learning Adaptive System for Speed Estimation in Sensor-less Induction Motor Drives

    Directory of Open Access Journals (Sweden)

    K Sedhuraman

    2012-12-01

    Full Text Available In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRNLAS is proposed. The model reference adaptive system (MRAS based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI. The non-linear mapping capability of a neural network (NN and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS. In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS and reactive power based (RP-MRNLAS. The reactive power- based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RPMRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP- MRNLAS technique is demonstrated

  11. Leptin Acts via Lateral Hypothalamic Area Neurotensin Neurons to Inhibit Orexin Neurons by Multiple GABA-Independent Mechanisms

    Science.gov (United States)

    Goforth, Paulette B.; Leinninger, Gina M.; Patterson, Christa M.

    2014-01-01

    The adipocyte-derived hormone leptin modulates neural systems appropriately for the status of body energy stores. Leptin inhibits lateral hypothalamic area (LHA) orexin (OX; also known as hypocretin)-producing neurons, which control feeding, activity, and energy expenditure, among other parameters. Our previous results suggest that GABAergic LHA leptin receptor (LepRb)-containing and neurotensin (Nts)-containing (LepRbNts) neurons lie in close apposition with OX neurons and control Ox mRNA expression. Here, we show that, similar to leptin, activation of LHA Nts neurons by the excitatory hM3Dq DREADD (designer receptor exclusively activated by designer drugs) hyperpolarizes membrane potential and suppresses action potential firing in OX neurons in mouse hypothalamic slices. Furthermore, ablation of LepRb from Nts neurons abrogated the leptin-mediated inhibition, demonstrating that LepRbNts neurons mediate the inhibition of OX neurons by leptin. Leptin did not significantly enhance GABAA-mediated inhibitory synaptic transmission, and GABA receptor antagonists did not block leptin-mediated inhibition of OX neuron activity. Rather, leptin diminished the frequency of spontaneous EPSCs onto OX neurons. Furthermore, leptin indirectly activated an ATP-sensitive potassium (KATP) channel in OX neurons, which was required for the hyperpolarization of OX neurons by leptin. Although Nts did not alter OX activity, galanin, which is coexpressed in LepRbNts neurons, inhibited OX neurons, whereas the galanin receptor antagonist M40 (galanin-(1–12)-Pro3-(Ala-Leu)2-Ala amide) prevented the leptin-induced hyperpolarization of OX cells. These findings demonstrate that leptin indirectly inhibits OX neurons by acting on LHA LepRbNts neurons to mediate two distinct GABA-independent mechanisms of inhibition: the presynaptic inhibition of excitatory neurotransmission and the opening of KATP channels. PMID:25143620

  12. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  13. System Identification of a Nonlinear Multivariable Steam Generator Power Plant Using Time Delay and Wavelet Neural Networks

    Directory of Open Access Journals (Sweden)

    Laila Khalilzadeh Ganjali-khani

    2013-01-01

    Full Text Available One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement, it is essential to have an accurate model for the steam generator of power plant. In this paper, an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years, artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.

  14. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed 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.

  15. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  16. Neural reflex regulation of systemic inflammation: potential new targets for sepsis therapy.

    Directory of Open Access Journals (Sweden)

    Ricardo eFernandez

    2014-12-01

    Full Text Available Sepsis progresses to multiple organ dysfunction due to the uncontrolled release of inflammatory mediators, and a growing body of evidence shows that neural signals play a significant role in modulating the immune response. Thus, similar toall other physiological systems, the immune system is both connected to and regulated by the central nervous system. The efferent arc consists of the activation of the hypothalamic–pituitary–adrenal axis, sympathetic activation, the cholinergic anti-inflammatory reflex, and the local release of physiological neuromodulators. Immunosensory activity is centered on the production of pro-inflammatory cytokines, signals that are conveyed to the brain through different pathways. The activation of peripheral sensory nerves, i.e., vagal paraganglia by the vagus nerve, and carotid body (CB chemoreceptors by the carotid/sinus nerve are broadly discussed here. Despite cytokine receptor expression in vagal afferent fibers, pro-inflammatory cytokines have no significant effect on vagus nerve activity. Thus, the CB may be the source of immunosensory inputs and incoming neural signals and, in fact, sense inflammatory mediators, playing a protective role during sepsis. Considering that CB stimulation increases sympathetic activity and adrenal glucocorticoids release, the electrical stimulation of arterial chemoreceptors may be suitable therapeutic approach for regulating systemic inflammation.

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

    Science.gov (United States)

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

    2015-09-15

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

  18. Thyroid hormone activation of retinoic acid synthesis in hypothalamic tanycytes

    Science.gov (United States)

    Stoney, Patrick N.; Helfer, Gisela; Rodrigues, Diana; Morgan, Peter J.

    2015-01-01

    Thyroid hormone (TH) is essential for adult brain function and its actions include several key roles in the hypothalamus. Although TH controls gene expression via specific TH receptors of the nuclear receptor class, surprisingly few genes have been demonstrated to be directly regulated by TH in the hypothalamus, or the adult brain as a whole. This study explored the rapid induction by TH of retinaldehyde dehydrogenase 1 (Raldh1), encoding a retinoic acid (RA)‐synthesizing enzyme, as a gene specifically expressed in hypothalamic tanycytes, cells that mediate a number of actions of TH in the hypothalamus. The resulting increase in RA may then regulate gene expression via the RA receptors, also of the nuclear receptor class. In vivo exposure of the rat to TH led to a significant and rapid increase in hypothalamic Raldh1 within 4 hours. That this may lead to an in vivo increase in RA is suggested by the later induction by TH of the RA‐responsive gene Cyp26b1. To explore the actions of RA in the hypothalamus as a potential mediator of TH control of gene regulation, an ex vivo hypothalamic rat slice culture method was developed in which the Raldh1‐expressing tanycytes were maintained. These slice cultures confirmed that TH did not act on genes regulating energy balance but could induce Raldh1. RA has the potential to upregulate expression of genes involved in growth and appetite, Ghrh and Agrp. This regulation is acutely sensitive to epigenetic changes, as has been shown for TH action in vivo. These results indicate that sequential triggering of two nuclear receptor signalling systems has the capability to mediate some of the functions of TH in the hypothalamus. GLIA 2016;64:425–439 PMID:26527258

  19. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network

    Directory of Open Access Journals (Sweden)

    Hongshan Yu

    2014-01-01

    Full Text Available Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.

  20. Automated system for load flow prediction in power substations using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Arlys Michel Lastre Aleaga

    2015-09-01

    Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

  1. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...... the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped...... sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0....

  2. PENGGUNAAN MOVING AVERAGE DENGAN METODE HYBRID ARTIFICIAL NEURAL NETWORK DAN FUZZY INFERENCE SYSTEM UNTUK PREDIKSI CUACA

    Directory of Open Access Journals (Sweden)

    Fahrur Rozi

    2016-12-01

    Full Text Available Kebutuhan akan prediksi sangat diperlukan diberbagai sektor kehidupan, salah satunya adalah mengenai prediksi cuaca. Prediksi mengenai cuaca dapat dilakukan dalam rentang waktu tertentu, sehingga untuk dapat memprediksi keadaan cuaca dalam rentang waktu tertentu penelitian ini akan menggunakan moving average dengan metode hybrid artificial neural network dan fuzzy inference system. Data yang digunakan berasal dari BMKG Karangploso, Malang dengan menggunakan empat buah parameter yang mempengaruhi kondisi cuaca, yaitu suhu, tekanan udara, kelembapan udara, dan kecepatan angin. Performa model menghasilkan tingkat akurasi mencapai 73.91 %.

  3. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed M. Wefky

    2010-04-01

    Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

  4. Visualization of suspicious lesions in breast MRI based on intelligent neural systems

    Science.gov (United States)

    Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

    2006-05-01

    Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

  5. Optimal system size for complex dynamics in random neural networks near criticality

    Energy Technology Data Exchange (ETDEWEB)

    Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)

    2013-12-15

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

  6. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

    Science.gov (United States)

    Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

    2013-06-01

    Objective. Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results. 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional ‘bias’ in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions

  7. An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands

    NARCIS (Netherlands)

    Vaz, A. G R; Elsinga, B.|info:eu-repo/dai/nl/372629601; van Sark, W. G J H M|info:eu-repo/dai/nl/074628526; Brito, M. C.

    2016-01-01

    In order to perform predictions of a photovoltaic (PV) system power production, a neural network architecture system using the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems as

  8. Biological channel modeling and implantable UWB antenna design for neural recording systems.

    Science.gov (United States)

    Bahrami, Hadi; Mirbozorgi, S Abdollah; Rusch, Leslie A; Gosselin, Benoit

    2015-01-01

    Ultrawideband (UWB) short-range communication systems have proved to be valuable in medical technology, particularly for implanted devices, due to their low-power consumption, low cost, small size, and high data rates. Neural activity monitoring in the brain requires high data rate (800 kb/s per neural sensor), and we target a system supporting a large number of sensors, in particular, aggregate transmission above 430 Mb/s (∼512 sensors). Knowledge of channel behavior is required to determine the maximum allowable power to 1) respect ANSI guidelines for avoiding tissue damage, and 2) respect FCC guidelines on unlicensed transmissions. We utilize a realistic model of the biological channel to inform the design of antennas for the implanted transmitter and the external receiver under these requirements. Antennas placement is examined under two scenarios having contrasting power constraints. Performance of the system within the biological tissues is examined via simulation and experiment. Our miniaturized antennas, 12 mm ×12 mm, need worst-case receiver sensitivities of -38 and -30.5 dBm for the first and second scenarios, respectively. These sensitivities allow us to successfully detect signals transmitted through tissues in the 3.1-10.6-GHz UWB band.

  9. Rapid-onset antidepressant efficacy of glutamatergic system modulators: the neural plasticity hypothesis of depression.

    Science.gov (United States)

    Wang, Jing; Jing, Liang; Toledo-Salas, Juan-Carlos; Xu, Lin

    2015-02-01

    Depression is a devastating psychiatric disorder widely attributed to deficient monoaminergic signaling in the central nervous system. However, most clinical antidepressants enhance monoaminergic neurotransmission with little delay but require 4-8 weeks to reach therapeutic efficacy, a paradox suggesting that the monoaminergic hypothesis of depression is an oversimplification. In contrast to the antidepressants targeting the monoaminergic system, a single dose of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine produces rapid (within 2 h) and sustained (over 7 days) antidepressant efficacy in treatment-resistant patients. Glutamatergic transmission mediated by NMDARs is critical for experience-dependent synaptic plasticity and learning, processes that can be modified indirectly by the monoaminergic system. To better understand the mechanisms of action of the new antidepressants like ketamine, we review and compare the monoaminergic and glutamatergic antidepressants, with emphasis on neural plasticity. The pathogenesis of depression may involve maladaptive neural plasticity in glutamatergic circuits that may serve as a new class of targets to produce rapid antidepressant effects.

  10. Optimization of steel casting feeding system based on BP neural network and genetic algorithm

    Directory of Open Access Journals (Sweden)

    Xue-dan Gong

    2016-05-01

    Full Text Available The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus inefficient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation (BP neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×106 mm3.

  11. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  12. Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

    Science.gov (United States)

    Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

    2015-08-01

    Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

  13. Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Leandro L. S. Linhares

    2015-01-01

    Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

  14. Hypothalamic Apelin/Reactive Oxygen Species Signaling Controls Hepatic Glucose Metabolism in the Onset of Diabetes

    Science.gov (United States)

    Drougard, Anne; Duparc, Thibaut; Brenachot, Xavier; Carneiro, Lionel; Gouazé, Alexandra; Fournel, Audren; Geurts, Lucie; Cadoudal, Thomas; Prats, Anne-Catherine; Pénicaud, Luc; Vieau, Didier; Lesage, Jean; Leloup, Corinne; Benani, Alexandre; Cani, Patrice D.; Valet, Philippe

    2014-01-01

    Abstract Aims: We have previously demonstrated that central apelin is implicated in the control of peripheral glycemia, and its action depends on nutritional (fast versus fed) and physiological (normal versus diabetic) states. An intracerebroventricular (icv) injection of a high dose of apelin, similar to that observed in obese/diabetic mice, increase fasted glycemia, suggesting (i) that apelin contributes to the establishment of a diabetic state, and (ii) the existence of a hypothalamic to liver axis. Using pharmacological, genetic, and nutritional approaches, we aim at unraveling this system of regulation by identifying the hypothalamic molecular actors that trigger the apelin effect on liver glucose metabolism and glycemia. Results: We show that icv apelin injection stimulates liver glycogenolysis and gluconeogenesis via an over-activation of the sympathetic nervous system (SNS), leading to fasted hyperglycemia. The effect of central apelin on liver function is dependent of an increased production of hypothalamic reactive oxygen species (ROS). These data are strengthened by experiments using lentiviral vector-mediated over-expression of apelin in hypothalamus of mice that present over-activation of SNS associated to an increase in hepatic glucose production. Finally, we report that mice fed a high-fat diet present major alterations of hypothalamic apelin/ROS signaling, leading to activation of glycogenolysis. Innovation/Conclusion: These data bring compelling evidence that hypothalamic apelin is one master switch that participates in the onset of diabetes by directly acting on liver function. Our data support the idea that hypothalamic apelin is a new potential therapeutic target to treat diabetes. Antioxid. Redox Signal. 20, 557–573. PMID:23879244

  15. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

    Directory of Open Access Journals (Sweden)

    Jinjun Tang

    Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.

  16. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado

    2011-05-01

    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  17. Mixed Mode Oscillations and Synchronous Activity in Noise Induced Modified Morris-Lecar Neural System

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Mondal, Argha; Teka, Wondimu W.

    The modified three-dimensional (3D) Morris-Lecar (M-L) model is very useful to understand the spiking activities of neurons. The present article addresses the random dynamical behavior of a modified M-L model driven by a white Gaussian noise with mean zero and unit spectral density. The applied stimulus can be expressed as a random term. Such random perturbations are represented by a white Gaussian noise current added through the electrical potential of membrane of the excitatory principal cells. The properties of the stochastic system (perturbed one) and noise induced mixed mode oscillation are analyzed. The Lyapunov spectrum is computed to present the nature of the system dynamics. The noise intensity is varied while keeping fixed the predominant parameters of the model in their ranges and also observed the changes in the dynamical behavior of the system. The dynamical synchronization is studied in the coupled M-L systems interconnected by excitatory and inhibitory neurons with noisy electrical coupling and verified with similarity functions. This result suggests the potential benefits of noise and noise induced oscillations which have been observed in real neurons and how that affects the dynamics of the neural model as well as the coupled systems. The analysis reports that the modified M-L system which has the limit cycle behavior can show a type of phase locking behavior which follows either period adding (i.e. 1:1, 2:1, 3:1, 4:1) sequences or Farey sequences. For the coupled neural systems, complete synchronization is shown for sufficient noisy coupling strength.

  18. A Neural Network Controller for Variable-Speed Variable-Pitch Wind Energy Conversion Systems Using Generalized Minimum Entropy Criterion

    Directory of Open Access Journals (Sweden)

    Mifeng Ren

    2014-01-01

    Full Text Available This paper considers the neural network controller design problem for variable pitch wind energy conversion systems (WECS with non-Gaussian wind speed disturbances in the stochastic distribution control framework. The approach here is used to directly model the unknown control law based on a fixed neural network (the number of layers and nodes in a neural network is fixed without the need to construct a separate model for the WECS. In order to characterize the randomness of the WECS, a generalized minimum entropy criterion is established to train connection weights of the neural network. For the train purpose, both kernel density estimation method and sliding window technique are adopted to estimate the PDF of tracking error and entropies. Due to the unknown process dynamics, the gradient of the objective function in a gradient-descent-type algorithm is estimated using an incremental perturbation method. The proposed approach is illustrated on a simulated WECS with non-Gaussian wind speed.

  19. Scaling up a chemically-defined aggregate-based suspension culture system for neural commitment of human pluripotent stem cells.

    Science.gov (United States)

    Miranda, Cláudia C; Fernandes, Tiago G; Diogo, M Margarida; Cabral, Joaquim M S

    2016-12-01

    The demand of high cell numbers for applications in cellular therapies and drug screening requires the development of scalable platforms capable to generating highly pure populations of tissue-specific cells from human pluripotent stem cells. In this work, we describe the scaling-up of an aggregate-based culture system for neural induction of human induced pluripotent stem cells (hiPSCs) under chemically-defined conditions. A combination of non-enzymatic dissociation and rotary agitation was successfully used to produce homogeneous populations of hiPSC aggregates with an optimal (140 μm) and narrow distribution of diameters (coefficient of variation of 21.6%). Scalable neural commitment of hiPSCs as 3D aggregates was performed in 50 mL spinner flasks, and the process was optimized using a factorial design approach, involving parameters such as agitation rate and seeding density. We were able to produce neural progenitor cell cultures, that at the end of a 6-day neural induction process contained less than 3% of Oct4-positive cells and that, after replating, retained more than 60% of Pax6-positive neural cells. The results here presented should set the stage for the future generation of a clinically relevant number of human neural progenitors for transplantation and other biomedical applications using controlled, automated and reproducible large-scale bioreactor culture systems. Copyright © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. A Robust Single Primate Neuroepithelial Cell Clonal Expansion System for Neural Tube Development and Disease Studies

    Directory of Open Access Journals (Sweden)

    Xiaoqing Zhu

    2016-02-01

    Full Text Available Developing a model of primate neural tube (NT development is important to promote many NT disorder studies in model organisms. Here, we report a robust and stable system to allow for clonal expansion of single monkey neuroepithelial stem cells (NESCs to develop into miniature NT-like structures. Single NESCs can produce functional neurons in vitro, survive, and extensively regenerate neuron axons in monkey brain. NT formation and NESC maintenance depend on high metabolism activity and Wnt signaling. NESCs are regionally restricted to a telencephalic fate. Moreover, single NESCs can turn into radial glial progenitors (RGPCs. The transition is accurately regulated by Wnt signaling through regulation of Notch signaling and adhesion molecules. Finally, using the “NESC-TO-NTs” system, we model the functions of folic acid (FA on NT closure and demonstrate that FA can regulate multiple mechanisms to prevent NT defects. Our system is ideal for studying NT development and diseases.

  1. Using Weightless Neural Networks for Vergence Control in an Artificial Vision System

    Directory of Open Access Journals (Sweden)

    Karin S. Komati

    2003-01-01

    Full Text Available This paper presents a methodology we have developed and used to implement an artificial binocular vision system capable of emulating the vergence of eye movements. This methodology involves using weightless neural networks (WNNs as building blocks of artificial vision systems. Using the proposed methodology, we have designed several architectures of WNN-based artificial vision systems, in which images captured by virtual cameras are used for controlling the position of the ‘foveae’ of these cameras (high-resolution region of the images captured. Our best architecture is able to control the foveae vergence movements with average error of only 3.58 image pixels, which is equivalent to an angular error of approximately 0.629°.

  2. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  3. Neural Network-Based Receiver in Band-Limited Communication System with MPPSK Modulation

    Directory of Open Access Journals (Sweden)

    Wang Zixin

    2018-01-01

    Full Text Available As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI. Therefore, a single hidden-layer neural network (NN-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.

  4. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  5. ARTIFICIAL NEURAL NETWORK BASED ULTRASONIC SENSOR SYSTEM FOR DETECTION OF ADULTERATION IN EDIBLE OIL

    Directory of Open Access Journals (Sweden)

    TONY GEORGE

    2017-06-01

    Full Text Available This paper presents the design, development and experimental validation of an ultrasonic sensor system for the detection of adulteration in edible oil. Variation of ultrasonic wave propagation characteristics like attenuation coefficient, reflection coefficient and velocity of propagation in pure and adulterated oil were used for developing the algorithm to detect the adulteration. Measurement cell was designed for operating ultrasonic transducer at 1 MHz using COMSOL 4.4. Artificial Neural Network (ANN based algorithm was also developed for improving the efficiency of the sensor system. It is found that this system can detect adulteration with an accuracy of 99.53% for sunflower oil added in pure coconut oil, whereas 98.82% for palm oil added in pure coconut oil.

  6. Hypothalamic Signaling Mechanisms in Hypertension

    OpenAIRE

    Carmichael, Casey Y.; Wainford, Richard D.

    2015-01-01

    The etiology of hypertension, a critical public health issue affecting one in three US adults, involves the integration of the actions of multiple organ systems, including the central nervous system. Increased activation of the central nervous system, driving enhanced sympathetic outflow and increased blood pressure, has emerged as a major contributor to the pathogenesis of hypertension. The hypothalamus is a key brain site acting to integrate central and peripheral inputs to ultimately impac...

  7. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  8. Extrapolating a hierarchy of building block systems towards future neural network organisms.

    Science.gov (United States)

    Jagers op Akkerhuis, G

    2001-01-01

    Is it possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this hierarchy can be used for predicting future life forms. The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings. This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms.

  9. Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity

    Science.gov (United States)

    Just, Marcel Adam; Keller, Timothy A.; Malave, Vicente L.; Kana, Rajesh K.; Varma, Sashank

    2012-01-01

    The underconnectivity theory of autism attributes the disorder to lower anatomical and functional systems connectivity between frontal and more posterior cortical processing. Here we review evidence for the theory and present a computational model of an executive functioning task (Tower of London) implementing the assumptions of underconnectivity. We make two modifications to a previous computational account of performance and brain activity in typical individuals in the Tower of London task (Newman et al., 2003): (1) the communication bandwidth between frontal and parietal areas was decreased and (2) the posterior centers were endowed with more executive capability (i.e., more autonomy, an adaptation is proposed to arise in response to the lowered frontal-posterior bandwidth). The autism model succeeds in matching the lower frontal-posterior functional connectivity (lower synchronization of activation) seen in fMRI data, as well as providing insight into behavioral response time results. The theory provides a unified account of how a neural dysfunction can produce a neural systems disorder and a psychological disorder with the widespread and diverse symptoms of autism. PMID:22353426

  10. An artificial neural network system to identify alleles in reference electropherograms.

    Science.gov (United States)

    Taylor, Duncan; Harrison, Ash; Powers, David

    2017-09-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. A biologically based neural system coordinates the joints and legs of a tetrapod.

    Science.gov (United States)

    Hunt, Alexander; Schmidt, Manuela; Fischer, Martin; Quinn, Roger

    2015-09-09

    A biologically inspired neural control system has been developed that coordinates a tetrapod trotting gait in the sagittal plane. The developed neuromechanical system is used to explore properties of connections in inter-leg and intra-leg coordination. The neural controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a planar biomechanical model of a rat with 14 joints, each actuated by a pair of antagonistic Hill muscle models. The controller generates tension in the muscles through activation of simulated motoneurons. The hind leg and inter-leg control networks are based on pathways discovered in cat research tuned to the kinematic motions of a rat. The foreleg network was developed by extrapolating analogous pathways from the hind legs. The formulated intra-leg and inter-leg networks properly coordinate the joints and produce motions similar to those of a walking rat. Changing the strength of a single inter-leg connection is sufficient to account for differences in phase timing in different trotting rats.

  12. Relationship between the hypothalamic-pituitary-adrenal-axis and fatty acid metabolism in recurrent depression

    NARCIS (Netherlands)

    Mocking, Roel J. T.; Ruhé, Henricus G.; Assies, Johanna; Lok, Anja; Koeter, Maarten W. J.; Visser, Ieke; Bockting, Claudi L. H.; Schene, Aart H.

    2013-01-01

    Alterations in hypothalamic-pituitary-adrenal (HPA)-axis activity and fatty acid (FA)-metabolism have been observed in (recurrent) major depressive disorder (MDD). Through the pathophysiological roles of FAs in the brain and cardiovascular system, a hypothesized relationship between HPA-axis

  13. Relationship between the hypothalamic-pituitary-adrenal-axis and fatty acid metabolism in recurrent depression

    NARCIS (Netherlands)

    Mocking, R. J. T.; Ruhe, E.; Assies, J.; Lok, A.; Koeter, M. W. J.; Visser, I.; Bockting, C. L. H.; Schene, A. H.

    Alterations in hypothalamic-pituitary-adrenal (HPA)-axis activity and fatty acid (FA)-metabolism have been observed in (recurrent) major depressive disorder (MDD). Through the pathophysiological roles of FAs in the brain and cardiovascular system, a hypothesized relationship between HPA-axis

  14. Use of artificial neural networks for analysis of complex physical systems

    Energy Technology Data Exchange (ETDEWEB)

    Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R.; Paez, T.L.

    1996-12-31

    Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs.

  15. 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/