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Sample records for control multipotent neural

  1. CHD7 cooperates with PBAF to control multipotent neural crest formation

    PubMed Central

    Bajpai, Ruchi; Chen, Denise A.; Rada-Iglesias, Alvaro; Zhang, Junmei; Xiong, Yiqin; Helms, Jill; Chang, Ching-Pin; Zhao, Yingming; Swigut, Tomek; Wysocka, Joanna

    2010-01-01

    Summary Heterozygous mutations in the gene encoding CHD7, an ATP-dependent chromatin remodeler result in a complex constellation of congenital anomalies called CHARGE syndrome. Here we show that in humans and in Xenopus, CHD7 is essential for the formation of multipotent migratory neural crest cells, a transient cell population that is ectodermal in origin, but undergoes a major gene expression reprogramming to acquire a remarkably broad differentiation potential and ability to migrate throughout the body to give rise to bones, cartilages, nerves, and cardiac structures. We demonstrate that CHD7 function is essential for activation of core components of neural crest transcriptional circuitry, including Sox9, Twist and Slug. Moreover, the major features of CHARGE are recapitulated in Xenopus embryo by the downregulation of CHD7 levels or overexpression of its catalytically inactive ATP-ase mutant. We further show that in human multipotent neural crest cells, CHD7 associates with a BRG1-containing complex PBAF, and both factors co-occupy a neural crest-specific distal SOX9 enhancer, as well as a novel genomic element located upstream from TWIST1 gene and marked by H3K4me1. Furthermore, in the embryo CHD7 and PBAF act synergistically to promote neural crest gene expression and cell migration. Our work identifies an evolutionary conserved role for CHD7 in orchestrating neural crest gene expression programs, provides insights into the synergistic regulation of distal genomic elements by two distinct chromatin remodelers, and illuminates the patho-embryology of CHARGE syndrome. PMID:20130577

  2. Confetti clarifies controversy: neural crest stem cells are multipotent.

    PubMed

    Bronner, Marianne

    2015-03-01

    Neural crest precursors generate diverse cell lineages during development, which have been proposed to arise either from multipotent precursor cells or pools of heterogeneous, restricted progenitors. Now in Cell Stem Cell, Baggiolini et al. (2015) perform rigorous in vivo lineage tracing to show that individual neural crest precursors are multipotent. PMID:25748927

  3. Premigratory and migratory neural crest cells are multipotent in vivo.

    PubMed

    Baggiolini, Arianna; Varum, Sandra; Mateos, José María; Bettosini, Damiano; John, Nessy; Bonalli, Mario; Ziegler, Urs; Dimou, Leda; Clevers, Hans; Furrer, Reinhard; Sommer, Lukas

    2015-03-01

    The neural crest (NC) is an embryonic stem/progenitor cell population that generates a diverse array of cell lineages, including peripheral neurons, myelinating Schwann cells, and melanocytes, among others. However, there is a long-standing controversy as to whether this broad developmental perspective reflects in vivo multipotency of individual NC cells or whether the NC is comprised of a heterogeneous mixture of lineage-restricted progenitors. Here, we resolve this controversy by performing in vivo fate mapping of single trunk NC cells both at premigratory and migratory stages using the R26R-Confetti mouse model. By combining quantitative clonal analyses with definitive markers of differentiation, we demonstrate that the vast majority of individual NC cells are multipotent, with only few clones contributing to single derivatives. Intriguingly, multipotency is maintained in migratory NC cells. Thus, our findings provide definitive evidence for the in vivo multipotency of both premigratory and migrating NC cells in the mouse. PMID:25748934

  4. FGF2 and insulin signaling converge to regulate cyclin D expression in multipotent neural stem cells.

    PubMed

    Adepoju, Adedamola; Micali, Nicola; Ogawa, Kazuya; Hoeppner, Daniel J; McKay, Ronald D G

    2014-03-01

    The ex vivo expansion of stem cells is making major contribution to biomedical research. The multipotent nature of neural precursors acutely isolated from the developing central nervous system has been established in a series of studies. Understanding the mechanisms regulating cell expansion in tissue culture would support their expanded use either in cell therapies or to define disease mechanisms. Basic fibroblast growth factor (FGF2) and insulin, ligands for tyrosine kinase receptors, are sufficient to sustain neural stem cells (NSCs) in culture. Interestingly, real-time imaging shows that these cells become multipotent every time they are passaged. Here, we analyze the role of FGF2 and insulin in the brief period when multipotent cells are present. FGF2 signaling results in the phosphorylation of Erk1/2, and activation of c-Fos and c-Jun that lead to elevated cyclin D mRNA levels. Insulin signals through the PI3k/Akt pathway to regulate cyclins at the post-transcriptional level. This precise Boolean regulation extends our understanding of the proliferation of multipotent NSCs and provides a basis for further analysis of proliferation control in the cell states defined by real-time mapping of the cell lineages that form the central nervous system.

  5. Vital dye labelling of Xenopus laevis trunk neural crest reveals multipotency and novel pathways of migration.

    PubMed

    Collazo, A; Bronner-Fraser, M; Fraser, S E

    1993-06-01

    Although the Xenopus embryo has served as an important model system for both molecular and cellular studies of vertebrate development, comparatively little is known about its neural crest. Here, we take advantage of the ease of manipulation and relative transparency of Xenopus laevis embryos to follow neural crest cell migration and differentiation in living embryos. We use two techniques to study the lineage and migratory patterns of frog neural crest cells: (1) injections of DiI or lysinated rhodamine dextran (LRD) into small populations of neural crest cells to follow movement and (2) injections of LRD into single cells to follow cell lineage. By using non-invasive approaches that allow observations in living embryos and control of the time and position of labelling, we have been able to expand upon the results of previous grafting experiments. Migration and differentiation of the labelled cells were observed over time in individual living embryos, and later in sections to determine precise position and morphology. Derivatives populated by the neural crest are the fins, pigment stripes, spinal ganglia, adrenal medulla, pronephric duct, enteric nuclei and the posterior portion of the dorsal aorta. In the rostral to mid-trunk levels, most neural crest cells migrate along two paths: a dorsal pathway into the fin, followed by presumptive fin cells, and a ventral pathway along the neural tube and notochord, followed by presumptive pigment, sensory ganglion, sympathetic ganglion and adrenal medullary cells. In the caudal trunk, two additional paths were noted. One group of cells moves circumferentially within the fin, in an arc from dorsal to ventral; another progresses ventrally to the anus and subsequently populates the ventral fin. By labelling individual precursor cells, we find that neural tube and neural crest cells often share a common precursor. The majority of clones contain labelled progeny cells in the dorsal fin. The remainder have progeny in multiple

  6. Methods for derivation of multipotent neural crest cells derived from human pluripotent stem cells

    PubMed Central

    Avery, John; Dalton, Stephen

    2016-01-01

    Summary Multipotent, neural crest cells (NCCs) produce a wide-range of cell types during embryonic development. This includes melanocytes, peripheral neurons, smooth muscle cells, osteocytes, chondrocytes and adipocytes. The protocol described here allows for highly-efficient differentiation of human pluripotent stem cells to a neural crest fate within 15 days. This is accomplished under feeder-free conditions, using chemically defined medium supplemented with two small molecule inhibitors that block glycogen synthase kinase 3 (GSK3) and bone morphogenic protein (BMP) signaling. This technology is well-suited as a platform to understand in greater detail the pathogenesis of human disease associated with impaired neural crest development/migration. PMID:25986498

  7. Exclusive multipotency and preferential asymmetric divisions in post-embryonic neural stem cells of the fish retina

    PubMed Central

    Centanin, Lázaro; Ander, Janina-J.; Hoeckendorf, Burkhard; Lust, Katharina; Kellner, Tanja; Kraemer, Isabel; Urbany, Cedric; Hasel, Eva; Harris, William A.; Simons, Benjamin D.; Wittbrodt, Joachim

    2014-01-01

    The potency of post-embryonic stem cells can only be addressed in the living organism, by labeling single cells after embryonic development and following their descendants. Recently, transplantation experiments involving permanently labeled cells revealed multipotent neural stem cells (NSCs) of embryonic origin in the medaka retina. To analyze whether NSC potency is affected by developmental progression, as reported for the mammalian brain, we developed an inducible toolkit for clonal labeling and non-invasive fate tracking. We used this toolkit to address post-embryonic stem cells in different tissues and to functionally differentiate transient progenitor cells from permanent, bona fide stem cells in the retina. Using temporally controlled clonal induction, we showed that post-embryonic retinal NSCs are exclusively multipotent and give rise to the complete spectrum of cell types in the neural retina. Intriguingly, and in contrast to any other vertebrate stem cell system described so far, long-term analysis of clones indicates a preferential mode of asymmetric cell division. Moreover, following the behavior of clones before and after external stimuli, such as injuries, shows that NSCs in the retina maintained the preference for asymmetric cell division during regenerative responses. We present a comprehensive analysis of individual post-embryonic NSCs in their physiological environment and establish the teleost retina as an ideal model for studying adult stem cell biology at single cell resolution. PMID:25142461

  8. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro.

    PubMed

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-10-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  9. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro

    PubMed Central

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-01-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  10. NG2-glia as multipotent neural stem cells – fact or fantasy?

    PubMed Central

    Richardson, William D; Young, Kaylene M; Tripathi, Richa B; McKenzie, Ian

    2011-01-01

    Summary Cycling glial precursors - “NG2-glia” - are abundant in the developing and mature central nervous system (CNS). During development they generate oligodendrocytes. In culture, they can revert to a multipotent state, suggesting that they might have latent stem cell potential that could be harnessed to treat neurodegenerative disease. This hope has been subdued recently by a series of fate mapping studies that cast NG2-glia as dedicated oligodendrocyte precursors in the healthy adult CNS - though rare neuron production in the piriform cortex remains a possibility. Following CNS damage, the repertoire of NG2-glia expands to include Schwann cells and possibly astrocytes – but so far not neurons. This confirms the central role of NG2-glia in myelin repair. The realization that oligodendrocyte generation continues throughout normal adulthood has seeded the idea that myelin genesis might also be involved in neural plasticity. We review these developments, highlighting areas of current interest, contention and speculation. PMID:21609823

  11. Neural differentiation of novel multipotent progenitor cells from cryopreserved human umbilical cord blood

    SciTech Connect

    Lee, Myoung Woo; Moon, Young Joon; Yang, Mal Sook; Kim, Sun Kyung; Jang, In Keun; Eom, Young-woo; Park, Joon Seong; Kim, Hugh C.; Song, Kye Yong; Park, Soon Cheol; Lim, Hwan Sub; Kim, Young Jin . E-mail: jin@lifecord.co.kr

    2007-06-29

    Umbilical cord blood (UCB) is a rich source of hematopoietic stem cells, with practical and ethical advantages. To date, the presence of other stem cells in UCB remains to be established. We investigated whether other stem cells are present in cryopreserved UCB. Seeded mononuclear cells formed adherent colonized cells in optimized culture conditions. Over a 4- to 6-week culture period, colonized cells gradually developed into adherent mono-layer cells, which exhibited homogeneous fibroblast-like morphology and immunophenotypes, and were highly proliferative. Isolated cells were designated 'multipotent progenitor cells (MPCs)'. Under appropriate conditions for 2 weeks, MPCs differentiated into neural tissue-specific cell types, including neuron, astrocyte, and oligodendrocyte. Differentiated cells presented their respective markers, specifically, NF-L and NSE for neurons, GFAP for astrocytes, and myelin/oligodendrocyte for oligodendrocytes. In this study, we successfully isolated MPCs from cryopreserved UCB, which differentiated into the neural tissue-specific cell types. These findings suggest that cryopreserved human UCB is a useful alternative source of neural progenitor cells, such as MPCs, for experimental and therapeutic applications.

  12. Extracellular matrix-regulated neural differentiation of human multipotent marrow progenitor cells enhances functional recovery after spinal cord injury

    PubMed Central

    Deng, Win-Ping; Yang, Chi-Chiang; Yang, Liang-Yo; Chen, Chun-Wei D.; Chen, Wei-Hong; Yang, Charn-Bing; Chen, Yu-Hsin; Lai, Wen-Fu T.; Renshaw, Perry F.

    2015-01-01

    BACKGROUND CONTEXT Recent advanced studies have demonstrated that cytokines and extracellular matrix (ECM) could trigger various types of neural differentiation. However, the efficacy of differentiation and in vivo transplantation has not yet thoroughly been investigated. PURPOSE To highlight the current understanding of the effects of ECM on neural differentiation of human bone marrow-derived multipotent progenitor cells (MPCs), regarding state-of-art cure for the animal with acute spinal cord injury (SCI), and explore future treatments aimed at neural repair. STUDY DESIGN A selective overview of the literature pertaining to the neural differentiation of the MSCs and experimental animals aimed at improved repair of SCI. METHODS Extracellular matrix proteins, tenascin-cytotactin (TN-C), tenascin-restrictin (TN-R), and chondroitin sulfate (CS), with the cytokines, nerve growth factor (NGF)/brain-derived neurotrophic factor (BDNF)/retinoic acid (RA) (NBR), were incorporated to induce transdifferentiation of human MPCs. Cells were treated with NBR for 7 days, and then TN-C, TN-R, or CS was added for 2 days. The medium was changed every 2 days. Twenty-four animals were randomly assigned to four groups with six animals in each group: one experimental and three controls. Animals received two (bilateral) injections of vehicle, MPCs, NBR-induced MPCs, or NBR/TN-C-induced MPCs into the lesion sites after SCI. Functional assessment was measured using the Basso, Beattie, and Bresnahan locomotor rating score. Data were analyzed using analysis of variance followed by Student-Newman-Keuls (SNK) post hoc tests. RESULTS Results showed that MPCs with the transdifferentiation of human MPCs to neurons were associated with increased messenger-RNA (mRNA) expression of neuronal markers including nestin, microtubule-associated protein (MAP) 2, glial fibrillary acidic protein, βIII tubulin, and NGF. Greater amounts of neuronal morphology appeared in cultures incorporated with TN-C and TN

  13. A clonal analysis of neural progenitors during axolotl spinal cord regeneration reveals evidence for both spatially restricted and multipotent progenitors.

    PubMed

    McHedlishvili, Levan; Epperlein, Hans H; Telzerow, Anja; Tanaka, Elly M

    2007-06-01

    Complete regeneration of the spinal cord occurs after tail regeneration in urodele amphibians such as the axolotl. Little is known about how neural progenitor cells are recruited from the mature tail, how they populate the regenerating spinal cord, and whether the neural progenitor cells are multipotent. To address these issues we used three types of cell fate mapping. By grafting green fluorescent protein-positive (GFP(+)) spinal cord we show that a 500 microm region adjacent to the amputation plane generates the neural progenitors for regeneration. We further tracked single nuclear-GFP-labeled cells as they proliferated during regeneration, observing their spatial distribution, and ultimately their expression of the progenitor markers PAX7 and PAX6. Most progenitors generate descendents that expand along the anterior/posterior (A/P) axis, but remain close to the dorsal/ventral (D/V) location of the parent. A minority of clones spanned multiple D/V domains, taking up differing molecular identities, indicating that cells can execute multipotency in vivo. In parallel experiments, bulk labeling of dorsally or ventrally restricted progenitor cells revealed that ventral cells at the distal end of the regenerating spinal cord switch to dorsal cell fates. Analysis of PAX7 and PAX6 expression along the regenerating spinal cord indicated that these markers are expressed in dorsal and lateral domains all along the spinal cord except at the distal terminus. These results suggest that neural progenitor identity is destabilized or altered in the terminal vesicle region, from which clear migration of cells into the surrounding blastema is also observed. PMID:17507409

  14. L-MYC Expression Maintains Self-Renewal and Prolongs Multipotency of Primary Human Neural Stem Cells.

    PubMed

    Li, Zhongqi; Oganesyan, Diana; Mooney, Rachael; Rong, Xianfang; Christensen, Matthew J; Shahmanyan, David; Perrigue, Patrick M; Benetatos, Joseph; Tsaturyan, Lusine; Aramburo, Soraya; Annala, Alexander J; Lu, Yang; Najbauer, Joseph; Wu, Xiwei; Barish, Michael E; Brody, David L; Aboody, Karen S; Gutova, Margarita

    2016-09-13

    Pre-clinical studies indicate that neural stem cells (NSCs) can limit or reverse CNS damage through direct cell replacement, promotion of regeneration, or delivery of therapeutic agents. Immortalized NSC lines are in growing demand due to the inherent limitations of adult patient-derived NSCs, including availability, expandability, potential for genetic modifications, and costs. Here, we describe the generation and characterization of a new human fetal NSC line, immortalized by transduction with L-MYC (LM-NSC008) that in vitro displays both self-renewal and multipotent differentiation into neurons, oligodendrocytes, and astrocytes. These LM-NSC008 cells were non-tumorigenic in vivo, and migrated to orthotopic glioma xenografts in immunodeficient mice. When administered intranasally, LM-NSC008 distributed specifically to sites of traumatic brain injury (TBI). These data support the therapeutic development of immortalized LM-NSC008 cells for allogeneic use in TBI and other CNS diseases. PMID:27546534

  15. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  16. Multipotent caudal neural progenitors derived from human pluripotent stem cells that give rise to lineages of the central and peripheral nervous system.

    PubMed

    Denham, Mark; Hasegawa, Kouichi; Menheniott, Trevelyan; Rollo, Ben; Zhang, Dongcheng; Hough, Shelley; Alshawaf, Abdullah; Febbraro, Fabia; Ighaniyan, Samiramis; Leung, Jessie; Elliott, David A; Newgreen, Donald F; Pera, Martin F; Dottori, Mirella

    2015-06-01

    The caudal neural plate is a distinct region of the embryo that gives rise to major progenitor lineages of the developing central and peripheral nervous system, including neural crest and floor plate cells. We show that dual inhibition of the glycogen synthase kinase 3β and activin/nodal pathways by small molecules differentiate human pluripotent stem cells (hPSCs) directly into a preneuroepithelial progenitor population we named "caudal neural progenitors" (CNPs). CNPs coexpress caudal neural plate and mesoderm markers, and, share high similarities to embryonic caudal neural plate cells in their lineage differentiation potential. Exposure of CNPs to BMP2/4, sonic hedgehog, or FGF2 signaling efficiently directs their fate to neural crest/roof plate cells, floor plate cells, and caudally specified neuroepithelial cells, respectively. Neural crest derived from CNPs differentiated to neural crest derivatives and demonstrated extensive migratory properties in vivo. Importantly, we also determined the key extrinsic factors specifying CNPs from human embryonic stem cell include FGF8, canonical WNT, and IGF1. Our studies are the first to identify a multipotent neural progenitor derived from hPSCs, that is the precursor for major neural lineages of the embryonic caudal neural tube.

  17. Sensitive Tumorigenic Potential Evaluation of Adult Human Multipotent Neural Cells Immortalized by hTERT Gene Transduction

    PubMed Central

    Jeong, Da Eun; Kim, Sung Soo; Song, Hye Jin; Pyeon, Hee Jang; Kang, Kyeongjin; Hong, Seung-Cheol; Nam, Do-Hyun; Joo, Kyeung Min

    2016-01-01

    Stem cells and therapeutic genes are emerging as a new therapeutic approach to treat various neurodegenerative diseases with few effective treatment options. However, potential formation of tumors by stem cells has hampered their clinical application. Moreover, adequate preclinical platforms to precisely test tumorigenic potential of stem cells are controversial. In this study, we compared the sensitivity of various animal models for in vivo stem cell tumorigenicity testing to identify the most sensitive platform. Then, tumorigenic potential of adult human multipotent neural cells (ahMNCs) immortalized by the human telomerase reverse transcriptase (hTERT) gene was examined as a stem cell model with therapeutic genes. When human glioblastoma (GBM) cells were injected into adult (4–6-week-old) Balb/c-nu, adult NOD/SCID, adult NOG, or neonate (1–2-week-old) NOG mice, the neonate NOG mice showed significantly faster tumorigenesis than that of the other groups regardless of intracranial or subcutaneous injection route. Two kinds of ahMNCs (682TL and 779TL) were primary cultured from surgical samples of patients with temporal lobe epilepsy. Although the ahMNCs were immortalized by lentiviral hTERT gene delivery (hTERT-682TL and hTERT-779TL), they did not form any detectable masses, even in the most sensitive neonate NOG mouse platform. Moreover, the hTERT-ahMNCs had no gross chromosomal abnormalities on a karyotype analysis. Taken together, our data suggest that neonate NOG mice could be a sensitive animal platform to test tumorigenic potential of stem cell therapeutics and that ahMNCs could be a genetically stable stem cell source with little tumorigenic activity to develop regenerative treatments for neurodegenerative diseases. PMID:27391353

  18. Sensitive Tumorigenic Potential Evaluation of Adult Human Multipotent Neural Cells Immortalized by hTERT Gene Transduction.

    PubMed

    Lee, Kee Hang; Nam, Hyun; Jeong, Da Eun; Kim, Sung Soo; Song, Hye Jin; Pyeon, Hee Jang; Kang, Kyeongjin; Hong, Seung-Cheol; Nam, Do-Hyun; Joo, Kyeung Min

    2016-01-01

    Stem cells and therapeutic genes are emerging as a new therapeutic approach to treat various neurodegenerative diseases with few effective treatment options. However, potential formation of tumors by stem cells has hampered their clinical application. Moreover, adequate preclinical platforms to precisely test tumorigenic potential of stem cells are controversial. In this study, we compared the sensitivity of various animal models for in vivo stem cell tumorigenicity testing to identify the most sensitive platform. Then, tumorigenic potential of adult human multipotent neural cells (ahMNCs) immortalized by the human telomerase reverse transcriptase (hTERT) gene was examined as a stem cell model with therapeutic genes. When human glioblastoma (GBM) cells were injected into adult (4-6-week-old) Balb/c-nu, adult NOD/SCID, adult NOG, or neonate (1-2-week-old) NOG mice, the neonate NOG mice showed significantly faster tumorigenesis than that of the other groups regardless of intracranial or subcutaneous injection route. Two kinds of ahMNCs (682TL and 779TL) were primary cultured from surgical samples of patients with temporal lobe epilepsy. Although the ahMNCs were immortalized by lentiviral hTERT gene delivery (hTERT-682TL and hTERT-779TL), they did not form any detectable masses, even in the most sensitive neonate NOG mouse platform. Moreover, the hTERT-ahMNCs had no gross chromosomal abnormalities on a karyotype analysis. Taken together, our data suggest that neonate NOG mice could be a sensitive animal platform to test tumorigenic potential of stem cell therapeutics and that ahMNCs could be a genetically stable stem cell source with little tumorigenic activity to develop regenerative treatments for neurodegenerative diseases. PMID:27391353

  19. Active control of the nucleation temperature enhances freezing survival of multipotent mesenchymal stromal cells.

    PubMed

    Lauterboeck, L; Hofmann, N; Mueller, T; Glasmacher, B

    2015-12-01

    Cryopreservation is a technique that has been extensively used for storage of multipotent mesenchymal stromal cells (MSCs) in regenerative medicine. Therefore, improving current cryopreservation procedures in terms of increasing cell viability and functionality is important. In this study, we optimized the cryopreservation protocol of MSCs derived from the common marmoset Callithrix jacchus (cj), which can be used as a non-human primate model in various pathological and transplantation studies and have a great potential for regenerative medicine. We have investigated the effect of the active control of the nucleation temperature using induced nucleation at a broad range of temperatures and two different dimethylsulfoxide concentrations (Me2SO, 5% (v/v) and 10%, (v/v)) to evaluate the overall effect on the viability, metabolic activity and recovery of cells after thawing. Survival rate and metabolic activity displayed an optimum when ice formation was induced at -10 °C. Cryomicroscopy studies indicated differences in ice crystal morphologies as well as differences in intracellular ice formation with different nucleation temperatures. High subzero nucleation temperatures resulted in larger extracellular ice crystals and cellular dehydration, whereas low subzero nucleation temperatures resulted in smaller ice crystals and intracellular ice formation. PMID:26499840

  20. Neural control of muscle

    NASA Technical Reports Server (NTRS)

    Max, S. R.; Markelonis, G. J.

    1983-01-01

    Cholinergic innervation regulates the physiological and biochemical properties of skeletal muscle. The mechanisms that appear to be involved in this regulation include soluble, neurally-derived polypeptides, transmitter-evoked muscle activity and the neurotransmitter, acetylcholine, itself. Despite extensive research, the interacting neural mechanisms that control such macromolecules as acetylcholinesterase, the acetylcholine receptor and glucose 6-phosphate dehydrogenase remain unclear. It may be that more simplified in vitro model systems coupled with recent dramatic advances in the molecular biology of neurally-regulated proteins will begin to allow researchers to unravel the mechanisms controlling the expression and maintenance of these macromolecules.

  1. The role of RhoA kinase inhibition in human placenta-derived multipotent cells on neural phenotype and cell survival.

    PubMed

    Wang, Chih-Hsiang; Wu, Chia-Ching; Hsu, Shan-Hui; Liou, Jun-Yang; Li, Yu-Wei; Wu, Kenneth K; Lai, Yiu-Kay; Yen, B Linju

    2013-04-01

    Current advances in stem cell biology have brought much hope for therapy of neuro-degenerative diseases. However, neural stem cells (NSCs) are rare adult stem cells, and the use of non-NSCs requires efficient and high-yielding lineage-specific differentiation prior to transplantation for efficacy. We report on the efficient differentiation of placental-derived multipotent cells (PDMCs) into a neural phenotype with use of Y-27632, a clinically compliant small molecular inhibitor of Rho kinase (ROCK) which is a major mediator of cytoskeleton dynamics. Y-27632 does not induce differentiation of PDMC toward the mesodermal lineages of adipogenesis and osteogenesis, but rather a neural-like morphology, with rapid development of cell extensions and processes within 24 h. Compared with conventional neurogenic differentiation agents, Y-27632 induces a higher percentage of neural-like cells in PDMCs without arresting proliferation or cell cycle dynamics. Y-27632-treated PDMCs express several neural lineage genes at the RNA and protein level, including nestin, MAP2, and GFAP. The effect of the ROCK inhibitor is cell-specific to PDMCs, and is mainly mediated through the ROCK2 isoform and its downstream target, myosin II. Our data suggest that ROCK inhibition and cytoskeletal rearrangement may allow for induction of a neural phenotype in PDMCs without compromising cell survival.

  2. Neural Architectures for Control

    NASA Technical Reports Server (NTRS)

    Peterson, James K.

    1991-01-01

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

  3. Chaotic neural control

    NASA Astrophysics Data System (ADS)

    Potapov, A.; Ali, M. K.

    2001-04-01

    We consider the problem of stabilizing unstable equilibria by discrete controls (the controls take discrete values at discrete moments of time). We prove that discrete control typically creates a chaotic attractor in the vicinity of an equilibrium. Artificial neural networks with reinforcement learning are known to be able to learn such a control scheme. We consider examples of such systems, discuss some details of implementing the reinforcement learning to controlling unstable equilibria, and show that the arising dynamics is characterized by positive Lyapunov exponents, and hence is chaotic. This chaos can be observed both in the controlled system and in the activity patterns of the controller.

  4. Neural networks for aircraft control

    NASA Technical Reports Server (NTRS)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  5. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  6. In vivo fate analysis reveals the multipotent and self-renewal capacities of Sox2+ neural stem cells in the adult hippocampus.

    PubMed

    Suh, Hoonkyo; Consiglio, Antonella; Ray, Jasodhara; Sawai, Toru; D'Amour, Kevin A; Gage, Fred H

    2007-11-01

    To characterize the properties of adult neural stem cells (NSCs), we generated and analyzed Sox2-GFP transgenic mice. Sox2-GFP cells in the subgranular zone (SGZ) express markers specific for progenitors, but they represent two morphologically distinct populations that differ in proliferation levels. Lentivirus- and retrovirus-mediated fate-tracing studies showed that Sox2+ cells in the SGZ have potential to give rise to neurons and astrocytes, revealing their multipotency at the population as well as at a single-cell level. A subpopulation of Sox2+ cells gives rise to cells that retain Sox2, highlighting Sox2+ cells as a primary source for adult NSCs. In response to mitotic signals, increased proliferation of Sox2+ cells is coupled with the generation of Sox2+ NSCs as well as neuronal precursors. An asymmetric contribution of Sox2+ NSCs may play an important role in maintaining the constant size of the NSC pool and producing newly born neurons during adult neurogenesis.

  7. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

    Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

  8. Neural Control of the Circulation

    ERIC Educational Resources Information Center

    Thomas, Gail D.

    2011-01-01

    The purpose of this brief review is to highlight key concepts about the neural control of the circulation that graduate and medical students should be expected to incorporate into their general knowledge of human physiology. The focus is largely on the sympathetic nerves, which have a dominant role in cardiovascular control due to their effects to…

  9. Nonlinear control with neural networks

    SciTech Connect

    Malik, S.A.

    1996-12-31

    Research results are presented to show the successful industrial application of neural networks in closed loop. Two distillation columns are used to demonstrate the effectiveness of nonlinear controllers. The two columns chosen for this purpose are very dissimilar in operating characteristics, and dynamic behavior. One of the columns is a crude column, and the second, a depropaniser, is a smaller column in a vapor recovery unit. In earlier work, neural networks had been presented as general function estimators, for prediction of stream compositions and the suitability of the various network architectures for this task had been investigated. This report reviews the successful application of neural networks, as feedback controllers, to large industrial distillation columns. 21 refs.

  10. Pigment Cell Progenitors in Zebrafish Remain Multipotent through Metamorphosis.

    PubMed

    Singh, Ajeet Pratap; Dinwiddie, April; Mahalwar, Prateek; Schach, Ursula; Linker, Claudia; Irion, Uwe; Nüsslein-Volhard, Christiane

    2016-08-01

    The neural crest is a transient, multipotent embryonic cell population in vertebrates giving rise to diverse cell types in adults via intermediate progenitors. The in vivo cell-fate potential and lineage segregation of these postembryonic progenitors is poorly understood, and it is unknown if and when the progenitors become fate restricted. We investigate the fate restriction in the neural crest-derived stem cells and intermediate progenitors in zebrafish, which give rise to three distinct adult pigment cell types: melanophores, iridophores, and xanthophores. By inducing clones in sox10-expressing cells, we trace and quantitatively compare the pigment cell progenitors at four stages, from embryogenesis to metamorphosis. At all stages, a large fraction of the progenitors are multipotent. These multipotent progenitors have a high proliferation ability, which diminishes with fate restriction. We suggest that multipotency of the nerve-associated progenitors lasting into metamorphosis may have facilitated the evolution of adult-specific traits in vertebrates. PMID:27453500

  11. Neural control of renin release.

    PubMed

    Stella, A; Golin, R; Zanchetti, A

    1989-02-01

    Among the major mechanisms controlling the renal release of renin, renal nerves are known to exert a direct stimulating action on juxtaglomerular cells that is mediated by beta-adrenoceptors. Activation of the renal nerves also exerts an important permissive role in order to amplify and possibly accelerate responses to stimuli affecting the vascular and macula densa mechanisms. Reduction of renal perfusion pressure, intravenous infusion of furosemide, and captopril administration cause a greater increase in renin release from innervated kidneys than from denervated kidneys. A complex interaction between neural and non-neural mechanisms in the control of renin secretion is suggested. Efferent renal nerve activity controlling the renin secretion rate is mainly under the inhibitory influence of vagal afferent fibers originating from the cardiopulmonary region. Recent experiments have demonstrated that a similar reflex tonic inhibition of renin secretion is also exerted by renal afferent fibers.

  12. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  13. A conserved germline multipotency program

    PubMed Central

    Juliano, Celina E.; Swartz, S. Zachary; Wessel, Gary M.

    2010-01-01

    The germline of multicellular animals is segregated from somatic tissues, which is an essential developmental process for the next generation. Although certain ecdysozoans and chordates segregate their germline during embryogenesis, animals from other taxa segregate their germline after embryogenesis from multipotent progenitor cells. An overlapping set of genes, including vasa, nanos and piwi, operate in both multipotent precursors and in the germline. As we propose here, this conservation implies the existence of an underlying germline multipotency program in these cell types that has a previously underappreciated and conserved function in maintaining multipotency. PMID:21098563

  14. Flexible body control using neural networks

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  15. Neural Network Controlled Visual Saccades

    NASA Astrophysics Data System (ADS)

    Johnson, Jeffrey D.; Grogan, Timothy A.

    1989-03-01

    The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.

  16. Attitude control of spacecraft using neural networks

    NASA Technical Reports Server (NTRS)

    Vadali, Srinivas R.; Krishnan, S.; Singh, T.

    1993-01-01

    This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

  17. Neural control of aggression in Drosophila.

    PubMed

    Hoopfer, Eric D

    2016-06-01

    Like most animal species, fruit flies fight to obtain and defend resources essential to survival and reproduction. Aggressive behavior in Drosophila is genetically specified and also strongly influenced by the fly's social context, past experiences and internal states, making it an excellent framework for investigating the neural mechanisms that regulate complex social behaviors. Here, I summarize our current knowledge of the neural control of aggression in Drosophila and discuss recent advances in understanding the sensory pathways that influence the decision to fight or court, the neuromodulatory control of aggression, the neural basis by which internal states can influence both fighting and courtship, and how social experience modifies aggressive behavior. PMID:27179788

  18. Neural-Network Controller For Vibration Suppression

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh Jong

    1995-01-01

    Neural-network-based adaptive-control system proposed for vibration suppression of flexible space structures. Controller features three-layer neural network and utilizes output feedback. Measurements generated by various sensors on structure. Feed forward path also included to speed up response in case plant exhibits predominantly linear dynamic behavior. System applicable to single-input single-output systems. Work extended to multiple-input multiple-output systems as well.

  19. Evolution of neural controllers for salamanderlike locomotion

    NASA Astrophysics Data System (ADS)

    Ijspeert, Auke J.

    1999-08-01

    This paper presents an experiment in which evolutionary algorithms are used for the development of neural controllers for salamander locomotion. The aim of the experiment is to investigate which kind of neural circuitry can produce the typical swimming and trotting gaits of the salamander, and to develop a synthetic approach to neurobiology by using genetic algorithms as design tool. A 2D bio-mechanical simulation of the salamander's body is developed whose muscle contraction is determined by the locomotion controller simulated as continuous-time neural networks. While the connectivity of the neural circuitry underlying locomotion in the salamander has not been decoded for the moment, the general organization of the designed neural circuits corresponds to that hypothesized by neurobiologist for the real animal. In particular, the locomotion controllers are based on a body central pattern generator (CPG) corresponding to a lamprey-like swimming controller as developed by Ekeberg, and are extended with a limb CPG for controlling the salamander's body. A genetic algorithm is used to instantiate synaptic weights of the connections within the limb CPG and from the limb CPG to the body CPG given a high level description of the desired gaits. A set of biologically plausible controllers are thus developed which can produce a neural activity and locomotion gaits very similar to those observed in the real salamander. By simply varying the external excitation applied to the network, the speed, direction and type of gait can be varied.

  20. Neural control of the immune system

    PubMed Central

    Sundman, Eva

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have suggested that vagus nerve stimulation can improve symptoms in human rheumatoid arthritis. These discoveries have generated an increased interest in bioelectronic medicine, i.e., therapeutic delivery of electrical impulses that activate nerves to regulate immune system function. Here, we discuss the physiology and potential therapeutic implications of neural immune control. PMID:25039084

  1. Neural networks and orbit control in accelerators

    SciTech Connect

    Bozoki, E.; Friedman, A.

    1994-07-01

    An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to `kicks` and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given.

  2. Twist1 Controls a Cell-Specification Switch Governing Cell Fate Decisions within the Cardiac Neural Crest

    PubMed Central

    Vincentz, Joshua W.; Firulli, Beth A.; Lin, Andrea; Spicer, Douglas B.; Howard, Marthe J.; Firulli, Anthony B.

    2013-01-01

    Neural crest cells are multipotent progenitor cells that can generate both ectodermal cell types, such as neurons, and mesodermal cell types, such as smooth muscle. The mechanisms controlling this cell fate choice are not known. The basic Helix-loop-Helix (bHLH) transcription factor Twist1 is expressed throughout the migratory and post-migratory cardiac neural crest. Twist1 ablation or mutation of the Twist-box causes differentiation of ectopic neuronal cells, which molecularly resemble sympathetic ganglia, in the cardiac outflow tract. Twist1 interacts with the pro-neural factor Sox10 via its Twist-box domain and binds to the Phox2b promoter to repress transcriptional activity. Mesodermal cardiac neural crest trans-differentiation into ectodermal sympathetic ganglia-like neurons is dependent upon Phox2b function. Ectopic Twist1 expression in neural crest precursors disrupts sympathetic neurogenesis. These data demonstrate that Twist1 functions in post-migratory neural crest cells to repress pro-neural factors and thereby regulate cell fate determination between ectodermal and mesodermal lineages. PMID:23555309

  3. Neural networks as a control methodology

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1990-01-01

    While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

  4. Integrated Neural Flight and Propulsion Control System

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  5. Neural predictive control for active buffet alleviation

    NASA Astrophysics Data System (ADS)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  6. Adaptive neural control of aeroelastic response

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  7. Neural Control of the Lower Urinary Tract

    PubMed Central

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  8. Toward Real Time Neural Net Flight Controllers

    NASA Technical Reports Server (NTRS)

    Jorgensen, C. C.; Mah, R. W.; Ross, J.; Lu, Henry, Jr. (Technical Monitor)

    1994-01-01

    NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

  9. Controlling neural network responsiveness: tradeoffs and constraints.

    PubMed

    Keren, Hanna; Marom, Shimon

    2014-01-01

    In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input-output relations and activity dependent dispersion of neuronal pair-wise correlations across the network. PMID:24808860

  10. Color control of printers by neural networks

    NASA Astrophysics Data System (ADS)

    Tominaga, Shoji

    1998-07-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of a neural network. The CIE-L*a*b* color system is used as the device-independent color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller determines the CMYK signals necessary to produce the desired L*a*b* values with a given printer. Our solution method for this control problem is based on a two-phase procedure which eliminates the need for UCR and GCR. The first phase determines a neural network as a model of the given printer, and the second phase determines the combined neural network system by combining the printer model and the controller in such a way that it represents an identity mapping in the L*a*b* color space. Then the network of the controller part realizes the mapping from the L*a*b* space to the CMYK space. Practical algorithms are presented in the form of multilayer feedforward networks. The feasibility of the proposed method is shown in experiments using a dye sublimation printer and an ink jet printer.

  11. The neural control of singing.

    PubMed

    Zarate, Jean Mary

    2013-01-01

    Singing provides a unique opportunity to examine music performance-the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI) research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: (1) neuroplasticity as a function of various lengths and types of training, (2) vocal amusia due to a compromised singing network, and (3) singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself.

  12. Analog compound orthogonal neural network control of robotic manipulators

    NASA Astrophysics Data System (ADS)

    Jun, Ye

    2005-12-01

    An analog compound orthogonal neural network is presented which is based on digital compound orthogonal neural networks. The compound neural network's control performance was investigated as applied to a robot control problem. The analog neural network is a Chebyshev neural network with a high speed-learning rate in an on-line manner. Its control algorithm does not relate to controlled plant models. The analog neural network is used as the feedforward controller, and PD is used as the feedback controller in the control system of robots. The excellent performance in system response, tracking accuracy, and robustness was verified through a simulation experiment applied to a robotic manipulator with friction and nonlinear disturbances. The trajectory tracking control showed results in satisfactory effectiveness. This analog neural controller provides a novel approach for the control of uncertain or unknown systems.

  13. Neural pathways underlying vocal control.

    PubMed

    Jürgens, Uwe

    2002-03-01

    Vocalization is a complex behaviour pattern, consisting of essentially three components: laryngeal activity, respiratory movements and supralaryngeal (articulatory) activity. The motoneurones controlling this behaviour are located in various nuclei in the pons (trigeminal motor nucleus), medulla (facial nucleus, nucl. ambiguus, hypoglossal nucleus) and ventral horn of the spinal cord (cervical, thoracic and lumbar region). Coordination of the different motoneurone pools is carried out by an extensive network comprising the ventrolateral parabrachial area, lateral pontine reticular formation, anterolateral and caudal medullary reticular formation, and the nucl. retroambiguus. This network has a direct access to the phonatory motoneurone pools and receives proprioceptive input from laryngeal, pulmonary and oral mechanoreceptors via the solitary tract nucleus and principal as well as spinal trigeminal nuclei. The motor-coordinating network needs a facilitatory input from the periaqueductal grey of the midbrain and laterally bordering tegmentum in order to be able to produce vocalizations. Voluntary control of vocalization, in contrast to completely innate vocal reactions, such as pain shrieking, needs the intactness of the forebrain. Voluntary control over the initiation and suppression of vocal utterances is carried out by the mediofrontal cortex (including anterior cingulate gyrus and supplementary as well as pre-supplementary motor area). Voluntary control over the acoustic structure of vocalizations is carried out by the motor cortex via pyramidal/corticobulbar as well as extrapyramidal pathways. The most important extrapyramidal pathway seems to be the connection motor cortex-putamen-substantia nigra-parvocellular reticular formation-phonatory motoneurones. The motor cortex depends upon a number of inputs for fulfilling its task. It needs a cerebellar input via the ventrolateral thalamus for allowing a smooth transition between consecutive vocal elements. It

  14. Neural control: Chaos control sets the pace

    NASA Astrophysics Data System (ADS)

    Schöll, Eckehard

    2010-03-01

    Even simple creatures, such as cockroaches, are capable of complex responses to changes in their environment. But robots usually require complicated dedicated control circuits to perform just a single action. Chaos control theory could allow simpler control strategies to realize more complex behaviour.

  15. Multipotent adult progenitor cells on an allograft scaffold facilitate the bone repair process

    PubMed Central

    LoGuidice, Amanda; Houlihan, Alison; Deans, Robert

    2016-01-01

    Multipotent adult progenitor cells are a recently described population of stem cells derived from the bone marrow stroma. Research has demonstrated the potential of multipotent adult progenitor cells for treating ischemic injury and cardiovascular repair; however, understanding of multipotent adult progenitor cells in orthopedic applications remains limited. In this study, we evaluate the osteogenic and angiogenic capacity of multipotent adult progenitor cells, both in vitro and loaded onto demineralized bone matrix in vivo, with comparison to mesenchymal stem cells, as the current standard. When compared to mesenchymal stem cells, multipotent adult progenitor cells exhibited a more robust angiogenic protein release profile in vitro and developed more extensive vasculature within 2 weeks in vivo. The establishment of this vascular network is critical to the ossification process, as it allows nutrient exchange and provides an influx of osteoprogenitor cells to the wound site. In vitro assays confirmed the multipotency of multipotent adult progenitor cells along mesodermal lineages and demonstrated the enhanced expression of alkaline phosphatase and production of calcium-containing mineral deposits by multipotent adult progenitor cells, necessary precursors for osteogenesis. In combination with a demineralized bone matrix scaffold, multipotent adult progenitor cells demonstrated enhanced revascularization and new bone formation in vivo in an orthotopic defect model when compared to mesenchymal stem cells on demineralized bone matrix or demineralized bone matrix–only control groups. The potent combination of angiogenic and osteogenic properties provided by multipotent adult progenitor cells appears to create a synergistic amplification of the bone healing process. Our results indicate that multipotent adult progenitor cells have the potential to better promote tissue regeneration and healing and to be a functional cell source for use in orthopedic applications

  16. Multipotent adult progenitor cells on an allograft scaffold facilitate the bone repair process.

    PubMed

    LoGuidice, Amanda; Houlihan, Alison; Deans, Robert

    2016-01-01

    Multipotent adult progenitor cells are a recently described population of stem cells derived from the bone marrow stroma. Research has demonstrated the potential of multipotent adult progenitor cells for treating ischemic injury and cardiovascular repair; however, understanding of multipotent adult progenitor cells in orthopedic applications remains limited. In this study, we evaluate the osteogenic and angiogenic capacity of multipotent adult progenitor cells, both in vitro and loaded onto demineralized bone matrix in vivo, with comparison to mesenchymal stem cells, as the current standard. When compared to mesenchymal stem cells, multipotent adult progenitor cells exhibited a more robust angiogenic protein release profile in vitro and developed more extensive vasculature within 2 weeks in vivo. The establishment of this vascular network is critical to the ossification process, as it allows nutrient exchange and provides an influx of osteoprogenitor cells to the wound site. In vitro assays confirmed the multipotency of multipotent adult progenitor cells along mesodermal lineages and demonstrated the enhanced expression of alkaline phosphatase and production of calcium-containing mineral deposits by multipotent adult progenitor cells, necessary precursors for osteogenesis. In combination with a demineralized bone matrix scaffold, multipotent adult progenitor cells demonstrated enhanced revascularization and new bone formation in vivo in an orthotopic defect model when compared to mesenchymal stem cells on demineralized bone matrix or demineralized bone matrix-only control groups. The potent combination of angiogenic and osteogenic properties provided by multipotent adult progenitor cells appears to create a synergistic amplification of the bone healing process. Our results indicate that multipotent adult progenitor cells have the potential to better promote tissue regeneration and healing and to be a functional cell source for use in orthopedic applications. PMID

  17. Immunological control of adult neural stem cells

    PubMed Central

    Gonzalez-Perez, Oscar; Quiñones-Hinojosa, Alfredo; Garcia-Verdugo, Jose Manuel

    2010-01-01

    Adult neurogenesis occurs only in discrete regions of adult central nervous system: the subventricular zone and the subgranular zone. These areas are populated by adult neural stem cells (aNSC) that are regulated by a number of molecules and signaling pathways, which control their cell fate choices, survival and proliferation rates. For a long time, it was believed that the immune system did not exert any control on neural proliferative niches. However, it has been observed that many pathological and inflammatory conditions significantly affect NSC niches. Even more, increasing evidence indicates that chemokines and cytokines play an important role in regulating proliferation, cell fate choices, migration and survival of NSCs under physiological conditions. Hence, the immune system is emerging is an important regulator of neurogenic niches in the adult brain, which may have clinical relevance in several brain diseases. PMID:20861925

  18. The neural optimal control hierarchy for motor control

    NASA Astrophysics Data System (ADS)

    DeWolf, T.; Eliasmith, C.

    2011-10-01

    Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.

  19. Neural control of maternal and paternal behaviors.

    PubMed

    Dulac, Catherine; O'Connell, Lauren A; Wu, Zheng

    2014-08-15

    Parental care, including feeding and protection of young, is essential for the survival as well as mental and physical well-being of the offspring. A large variety of parental behaviors has been described across species and sexes, raising fascinating questions about how animals identify the young and how brain circuits drive and modulate parental displays in males and females. Recent studies have begun to uncover a striking antagonistic interplay between brain systems underlying parental care and infant-directed aggression in both males and females, as well as a large range of intrinsic and environmentally driven neural modulation and plasticity. Improved understanding of the neural control of parental interactions in animals should provide novel insights into the complex issue of human parental care in both health and disease.

  20. Neural control of maternal and paternal behaviors

    PubMed Central

    Dulac, Catherine; O’Connell, Lauren A.; Wu, Zheng

    2014-01-01

    Parental care, including feeding and protection of young, is essential for the survival as well as mental and physical well-being of the offspring. A large variety of parental behaviors has been described across species and sexes, raising fascinating questions about how animals identify the young and how brain circuits drive and modulate parental displays in males and females. Recent studies have begun to uncover a striking antagonistic interplay between brain systems underlying parental care and infant-directed aggression in both males and females, as well as a large range of intrinsic and environmentally driven neural modulation and plasticity. Improved understanding of the neural control of parental interactions in animals should provide novel insights into the complex issue of human parental care in both health and disease. PMID:25124430

  1. Cognitive Control Signals for Neural Prosthetics

    NASA Astrophysics Data System (ADS)

    Musallam, S.; Corneil, B. D.; Greger, B.; Scherberger, H.; Andersen, R. A.

    2004-07-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patient's preferences and motivation.

  2. Microturbine control based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Yan, Shijie; Bian, Chunyuan; Wang, Zhiqiang

    2006-11-01

    As microturbine generator (MTG) is a clean, efficient, low cost and reliable energy supply system. From outside characteristics of MTG, it is multi-variable, time-varying and coupling system, so it is difficult to be identified on-line and conventional control law adopted before cannot achieve desirable result. A novel fuzzy-neural networks (FNN) control algorithm was proposed in combining with the conventional PID control. In the paper, IF-THEN rules for tuning were applied by a first-order Sugeno fuzzy model with seven fuzzy rules and the membership function was given as the continuous GAUSSIAN function. Some sample data were utilized to train FNN. Through adjusting shape of membership function and weight continually, objective of auto-tuning fuzzy-rules can be achieved. The FNN algorithm had been applied to "100kW Microturbine control and power converter system". The results of simulation and experiment are shown that the algorithm can work very well.

  3. Controlling chaotic convection using neural nets-theory and experiments.

    PubMed

    Bau, Haim H.; Yuen, Po Ki

    1998-04-01

    An exploratory study is conducted to assess the feasibility of using neural networks to control flow patterns and to evaluate the performance of these controllers. Neural networks were used to control (suppress) chaotic convection both in experiments and in a theoretical model of a thermal convection loop. It is demonstrated that the neural network controller can successfully cause the flow to behave in a desired way. The performance of the neural network controllers was compared with that of previously used conventional linear proportional controllers.

  4. Towards practical control design using neural computation

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Garg, Sanjay; Mattern, Duane; Merrill, Walter

    1991-01-01

    The objective is to develop neural network based control design techniques which address the issue of performance/control effort tradeoff. Additionally, the control design needs to address the important issue if achieving adequate performance in the presence of actuator nonlinearities such as position and rate limits. These issues are discussed using the example of aircraft flight control. Given a set of pilot input commands, a feedforward net is trained to control the vehicle within the constraints imposed by the actuators. This is achieved by minimizing an objective function which is the sum of the tracking errors, control input rates and control input deflections. A tradeoff between tracking performance and control smoothness is obtained by varying, adaptively, the weights of the objective function. The neurocontroller performance is evaluated in the presence of actuator dynamics using a simulation of the vehicle. Appropriate selection of the different weights in the objective function resulted in the good tracking of the pilot commands and smooth neurocontrol. An extension of the neurocontroller design approach is proposed to enhance its practicality.

  5. Neural networks for LED color control

    NASA Astrophysics Data System (ADS)

    Ashdown, Ian E.

    2004-01-01

    The design and implementation of an architectural dimming control for multicolor LED-based lighting fixtures is complicated by the need to maintain a consistent color balance under a wide variety of operating conditions. Factors to consider include nonlinear relationships between luminous flux intensity and drive current, junction temperature dependencies, LED manufacturing tolerances and binning parameters, device aging characteristics, variations in color sensor spectral responsitivities, and the approximations introduced by linear color space models. In this paper we formulate this problem as a nonlinear multidimensional function, where maintaining a consistent color balance is equivalent to determining the hyperplane representing constant chromaticity. To be useful for an architectural dimming control design, this determination must be made in real time as the lighting fixture intensity is adjusted. Further, the LED drive current must be continuously adjusted in response to color sensor inputs to maintain constant chromaticity for a given intensity setting. Neural networks are known to be universal approximators capable of representing any continuously differentiable bounded function. We therefore use a radial basis function neural network to represent the multidimensional function and provide the feedback signals needed to maintain constant chromaticity. The network can be trained on the factory floor using individual device measurements such as spectral radiant intensity and color sensor characteristics. This provides a flexible solution that is mostly independent of LED manufacturing tolerances and binning parameters.

  6. Epigenetic Regulation in Neural Crest Development

    PubMed Central

    Hu, Na; Strobl-Mazzulla, Pablo H.; Bronner, Marianne E.

    2014-01-01

    The neural crest is a migratory and multipotent cell population that plays a crucial many aspects of embryonic development. In all vertebrate embryos, these cells emerge from the dorsal neural tube then migrate long distances to different regions of the body, where they contribute to formation of many cell types and structures. These include much of the peripheral nervous system, craniofacial skeleton, smooth muscle, and pigmentation of the skin. The best-studied regulatory events guiding neural crest development are mediated by transcription factors and signaling molecules. In recent years, however, growing evidence supports an important role for epigenetic regulation as an additional mechanism for controlling the timing and level of gene expression at different stages of neural crest development. Here, we summarize the process of neural crest formation, with focus on the role of epigenetic regulation in neural crest specification, migration, and differentiation as well as in neural crest related birth defects and diseases. PMID:25446277

  7. Understanding the brain by controlling neural activity

    PubMed Central

    Krug, Kristine; Salzman, C. Daniel; Waddell, Scott

    2015-01-01

    Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been combined with electrical devices, targeted genetically encoded tools and neurochemical approaches to manipulate information processing in the brain. The ability to control brain activity in these ways not only deepens our understanding of brain function but also provides new avenues for clinical intervention, particularly in conditions where brain processing has gone awry. PMID:26240417

  8. Fuzzy-neural control of an aircraft tracking camera platform

    NASA Technical Reports Server (NTRS)

    Mcgrath, Dennis

    1994-01-01

    A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

  9. Are muscle synergies useful for neural control?

    PubMed

    de Rugy, Aymar; Loeb, Gerald E; Carroll, Timothy J

    2013-01-01

    The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore

  10. Isolation, characterization, and differentiation of human multipotent dermal stem cells.

    PubMed

    Li, Ling; Fukunaga-Kalabis, Mizuho; Herlyn, Meenhard

    2013-01-01

    Skin, as the body's largest organ, has been extensively used to study adult stem cells. Most previous skin-related studies have focused on stem cells isolated from hair follicles and from keratinocytes. Here we present a protocol to isolate multipotent neural crest stem-like dermis-derived stem cells (termed dermal stem cells or DSCs) from human neonatal foreskins. DSCs grow like neural spheres in human embryonic stem cell medium and gain the ability to self-renew and differentiate into several cell lineages including melanocytes, neuronal cells, Schwann cells, smooth muscle cells, adipocytes, and chondrocytes. These cells express neural crest stem cell markers (NGFRp75 and nestin) as well as an embryonic stem cell marker (OCT4).

  11. Multi-layer neural networks for robot control

    NASA Technical Reports Server (NTRS)

    Pourboghrat, Farzad

    1989-01-01

    Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

  12. Real-time Adaptive Control Using Neural Generalized Predictive Control

    NASA Technical Reports Server (NTRS)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  13. Control of neural chaos by synaptic noise.

    PubMed

    Cortes, J M; Torres, J J; Marro, J

    2007-02-01

    We study neural automata - or neurobiologically inspired cellular automata - which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems.

  14. Accelerator diagnosis and control by Neural Nets

    SciTech Connect

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  15. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  16. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

    It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

  17. Distributed memory approaches for robotic neural controllers

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1990-01-01

    The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

  18. Multipotent progenitor cells isolated from adult human pancreatic tissue.

    PubMed

    Todorov, I; Nair, I; Ferreri, K; Rawson, J; Kuroda, A; Pascual, M; Omori, K; Valiente, L; Orr, C; Al-Abdullah, I; Riggs, A; Kandeel, F; Mullen, Y

    2005-10-01

    The supply of islet cells is a limiting factor for the widespread application of islet transplantation of type-1 diabetes. Islets constitute 1% to 2% of pancreatic tissue, leaving approximately 98% as discard after islet isolation and purification. In this report we present our data on the isolation of multipotent progenitor cells from discarded adult human pancreatic tissue. The collected cells from discarded nonislet fractions, after enzymatic digestion and gradient purification of islets, were dissociated for suspension culture in a serum-free medium. The cell clusters grown to a size of 100 to 150 mum contained cells staining for stage-specific embryonic antigens, but not insulin or C-peptide. To direct cell differentiation toward islets, clusters were recultured in a pancreatic differentiation medium. Insulin and C-peptide-positive cells by immunocytochemistry appeared within a week, reaching over 10% of the cell population. Glucagon and somatostatin-positive cells were also detected. The cell clusters were found to secrete insulin in response to glucose stimulation. Cells from the same clusters also had the capacity for differentiation into neural cells, as documented by staining for neural and glial cell markers when cultured as monolayers in media containing neurotrophic factors. These data suggest that multipotent pancreatic progenitor cells exist within the human pancreatic tissue that is typically discarded during islet isolation procedures. These adult progenitor cells can be successfully differentiated into insulin-producing cells, and thus they have the potential for treatment of type-1 diabetes mellitus. PMID:16298614

  19. Controlling natural convection in a closed thermosyphon using neural networks

    NASA Astrophysics Data System (ADS)

    Cammarata, L.; Fichera, A.; Pagano, A.

    . The aim of this paper is to present a neural network-based approach to identification and control of a rectangular natural circulation loop. The first part of the paper defines a NARMAX model for the prediction of the experimental oscillating behavior characterizing the fluid temperature. The model has been generalized and implemented by means of a Multilayer Perceptron Neural Network that has been trained to simulate the system experimental dynamics. In the second part of the paper, the NARMAX model has been used to simulate the plant during the training of another neural network aiming to suppress the undesired oscillating behavior of the system. In order to define the neural controller, a cascade of several couples of neural networks representing both the system and the controller has been used, the number of couples coinciding with the number of steps in which the control action is exerted.

  20. Neural Control of the Immune System

    ERIC Educational Resources Information Center

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

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

  1. Neural control of blood glucose level.

    PubMed

    Niijima, A

    1986-01-01

    All of the experimental results described above can be categorized as follows: the relationship between glucose levels and pancreatic and adrenal nerve activities; innervations of the liver and their role in the regulation of blood glucose level; central integration of blood glucose level; glucose-sensitive afferent nerve fibers in the liver and regulation of blood glucose; oral and intestinal inputs involved in reflex control of blood glucose level. We showed that an increase in blood glucose content produced an increase in the activity of the pancreatic branch of the vagus nerve, whereas it induced a decrease in the activity of the adrenal nerve. It was also shown that a decrease in blood glucose activated the sympatho-adrenal system and suppressed the vago-pancreatic system. It seems rational that these responses are involved in the maintenance of blood glucose level. Studies on the innervation of the liver led us to a conclusion that sympathetic innervation of the liver might play a role in eliciting a prompt hyperglycemic response through liberation of norepinephrine from the nerve terminals, and that the vagal innervation synergically worked with the humoral factor (insulin) for glycogen synthesis in the hyperglycemic condition. The glucose-sensitive afferents from the liver seem to initiate a reflex control of blood glucose level. The gustatory information on EIR response, reported by STEFFENS, is supported by the electrophysiological observations. MEI's reports also indicated the importance of information from the intestinal glucoreceptors in the reflex control of insulin secretion. The role of integrative functions of the hypothalamus and brainstem through neuronal networks on neural control of blood glucose levels is also evident. A schematic diagram of the nervous networks involved in the regulation of the blood glucose levels is shown in Fig. 3. PMID:3550186

  2. Control chart pattern recognition using a back propagation neural network

    NASA Astrophysics Data System (ADS)

    Spoerre, Julie K.; Perry, Marcus B.

    2000-10-01

    In this paper, control chart pattern recognition using artificial neural networks is presented. An important motivation of this research is the growing interest in intelligent manufacturing systems, specifically in the area of Statistical Process Control (SPC). On-line automated process analysis is an important area of research since it allows the interfacing of process control with Computer Integrated Manufacturing (CIM) techniques. A back propagation artificial neural network is used to model X-bar quality control charts and identify process instability situations as specified by the Western Electric Statistical Quality Control handbook. Results indicate that the performance of the back propagation neural network is very accurate in identifying these control chart patterns. This work is significant in that the neural network output can serve as a link to process parameters in a closed-loop control system. In this way, adjustments to the process can be made on-line and quality problems averted.

  3. Thermoelastic steam turbine rotor control based on neural network

    NASA Astrophysics Data System (ADS)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  4. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    NASA Astrophysics Data System (ADS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-06-01

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

  5. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    SciTech Connect

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-06-12

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

  6. Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (Phospho)Proteomic Profiling.

    PubMed

    Singec, Ilyas; Crain, Andrew M; Hou, Junjie; Tobe, Brian T D; Talantova, Maria; Winquist, Alicia A; Doctor, Kutbuddin S; Choy, Jennifer; Huang, Xiayu; La Monaca, Esther; Horn, David M; Wolf, Dieter A; Lipton, Stuart A; Gutierrez, Gustavo J; Brill, Laurence M; Snyder, Evan Y

    2016-09-13

    Controlled differentiation of human embryonic stem cells (hESCs) can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs). This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites) provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families), phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt. PMID:27569059

  7. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  8. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

    Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  9. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

  10. Neural Networks for Modeling and Control of Particle Accelerators

    DOE PAGES

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

  11. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  12. Recognition with self-control in neural networks

    NASA Astrophysics Data System (ADS)

    Lewenstein, Maciej; Nowak, Andrzej

    1989-10-01

    We present a theory of fully connected neural networks that incorporates mechanisms of dynamical self-control of recognition process. Using a functional integral technique, we formulate mean-field dynamics for such systems.

  13. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  14. Factors controlling cardiac neural crest cell migration

    PubMed Central

    Hutson, Mary R

    2010-01-01

    Cardiac neural crest cells originate as part of the postotic caudal rhombencephalic neural crest stream. Ectomesenchymal cells in this stream migrate to the circumpharyngeal ridge and then into the caudal pharyngeal arches where they condense to form first a sheath and then the smooth muscle tunics of the persisting pharyngeal arch arteries. A subset of the cells continues migrating into the cardiac outflow tract where they will condense to form the aorticopulmonary septum. Cell signaling, extracellular matrix and cell-cell contacts are all critical for the initial migration, pauses, continued migration and condensation of these cells. This Review elucidates what is currently known about these factors. PMID:20890117

  15. Neural control of helicopter blade-vortex interaction noise

    NASA Astrophysics Data System (ADS)

    Glaessel, Holger; Kloeppel, Valentin; Rudolph, Stephan

    2001-06-01

    Significant reduction of helicopter blade-vortex interaction (BVI) noise is currently one of the most advanced research topics in the helicopter industry. This is due to the complex flow, the close aerodynamic and structural coupling, and the interaction of the blades with the trailing edge vortices. Analytical and numerical modeling techniques are therefore currently still far from a sufficient degree of accuracy to obtain satisfactory results using classical model based control concepts. Neural networks with a proven potential to learn nonlinear relationships implicitly encoded in a training data set are therefore an appropriate and complementary technique for the alternative design of a nonlinear controller for BVI noise reduction. For nonlinear and adaptive control different neural control strategies have been proposed. Two possible approaches, a direct and an indirect neural controller are described. In indirect neural control, the plant has to be identified first by training a network with measured data. The plant network is then used to train the controller network. On the other hand the direct control approach does not rely on an explicit plant model, instead a specific training algorithm (like reinforcement learning) uses the information gathered from interactions with the environment. In the investigation of the BVI noise phenomena, helicopter developers have undertaken substantial efforts in full scale flight tests and wind tunnel experiments. Data obtained in these experiments have been adequately preprocessed using wavelet analysis and filtering techniques and are then used in the design of a neural controller. Neural open-loop control and neural closed-loop control concepts for the BVI noise reduction problem are conceived, simulated and compared against each other in this work in the above mentioned framework.

  16. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  17. Establishment of oct4:gfp transgenic zebrafish line for monitoring cellular multipotency by GFP fluorescence.

    PubMed

    Kato, Hiroyuki; Abe, Kota; Yokota, Shinpei; Matsuno, Rinta; Mikekado, Tsuyoshi; Yokoi, Hayato; Suzuki, Tohru

    2015-01-01

    The establishment of induced pluripotent stem (iPS) cell technology in fish could facilitate the establishment of novel cryopreservation techniques for storing selected aquaculture strains as frozen cells. In order to apply iPS cell technology to fish, we established a transgenic zebrafish line, Tg(Tru.oct4:EGFP), using green fluorescent protein (GFP) expression under the control of the oct4 gene promoter as a marker to evaluate multipotency in iPS cell preparations. We used the oct4 promoter from fugu (Takifugu rubripes) due to the compact nature of the fugu genome and to facilitate future applications of this technology in marine fishes. During embryogenesis, maternal GFP fluorescence was observed at the cleavage stage and zygotic GFP expression was observed from the start of the shield stage until approximately 24 h after fertilization. gfp messenger RNA (mRNA) was expressed by whole embryonic cells at the shield stage, and then restricted to the caudal neural tube in the latter stages of embryogenesis. These observations showed that GFP fluorescence and the regulation of gfp mRNA expression by the exogenous fugu oct4 promoter are well suited for monitoring endogenous oct4 mRNA expression in embryos. Bisulfite sequencing revealed that the rate of CpG methylation in the transgenic oct4 promoter was high in adult cells (98%) and low in embryonic cells (37%). These findings suggest that, as with the endogenous oct4 promoter, demethylation and methylation both take place normally in the transgenic oct4 promoter during embryogenesis. The embryonic cells harvested at the shield stage formed embryonic body-like cellular aggregates and maintained GFP fluorescence for 6 d when cultured on Transwell-COL Permeable Supports or a feeder layer of adult fin cells. Loss of GFP fluorescence by cultured cells was correlated with cellular differentiation. We consider that the Tg(Tru.oct4:EGFP) zebrafish line established here is well suited for monitoring multipotency in

  18. Establishment of oct4:gfp transgenic zebrafish line for monitoring cellular multipotency by GFP fluorescence.

    PubMed

    Kato, Hiroyuki; Abe, Kota; Yokota, Shinpei; Matsuno, Rinta; Mikekado, Tsuyoshi; Yokoi, Hayato; Suzuki, Tohru

    2015-01-01

    The establishment of induced pluripotent stem (iPS) cell technology in fish could facilitate the establishment of novel cryopreservation techniques for storing selected aquaculture strains as frozen cells. In order to apply iPS cell technology to fish, we established a transgenic zebrafish line, Tg(Tru.oct4:EGFP), using green fluorescent protein (GFP) expression under the control of the oct4 gene promoter as a marker to evaluate multipotency in iPS cell preparations. We used the oct4 promoter from fugu (Takifugu rubripes) due to the compact nature of the fugu genome and to facilitate future applications of this technology in marine fishes. During embryogenesis, maternal GFP fluorescence was observed at the cleavage stage and zygotic GFP expression was observed from the start of the shield stage until approximately 24 h after fertilization. gfp messenger RNA (mRNA) was expressed by whole embryonic cells at the shield stage, and then restricted to the caudal neural tube in the latter stages of embryogenesis. These observations showed that GFP fluorescence and the regulation of gfp mRNA expression by the exogenous fugu oct4 promoter are well suited for monitoring endogenous oct4 mRNA expression in embryos. Bisulfite sequencing revealed that the rate of CpG methylation in the transgenic oct4 promoter was high in adult cells (98%) and low in embryonic cells (37%). These findings suggest that, as with the endogenous oct4 promoter, demethylation and methylation both take place normally in the transgenic oct4 promoter during embryogenesis. The embryonic cells harvested at the shield stage formed embryonic body-like cellular aggregates and maintained GFP fluorescence for 6 d when cultured on Transwell-COL Permeable Supports or a feeder layer of adult fin cells. Loss of GFP fluorescence by cultured cells was correlated with cellular differentiation. We consider that the Tg(Tru.oct4:EGFP) zebrafish line established here is well suited for monitoring multipotency in

  19. A rule-based neural controller for inverted pendulum system.

    PubMed

    Hao, J; Vandewalle, J; Tan, S

    1993-03-01

    This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

  20. Genetic Control of Active Neural Circuits

    PubMed Central

    Reijmers, Leon; Mayford, Mark

    2009-01-01

    The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long-lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory. PMID:20057936

  1. Distributed neural control of a hexapod walking vehicle

    NASA Technical Reports Server (NTRS)

    Beer, R. D.; Sterling, L. S.; Quinn, R. D.; Chiel, H. J.; Ritzmann, R.

    1989-01-01

    There has been a long standing interest in the design of controllers for multilegged vehicles. The approach is to apply distributed control to this problem, rather than using parallel computing of a centralized algorithm. Researchers describe a distributed neural network controller for hexapod locomotion which is based on the neural control of locomotion in insects. The model considers the simplified kinematics with two degrees of freedom per leg, but the model includes the static stability constraint. Through simulation, it is demonstrated that this controller can generate a continuous range of statically stable gaits at different speeds by varying a single control parameter. In addition, the controller is extremely robust, and can continue the function even after several of its elements have been disabled. Researchers are building a small hexapod robot whose locomotion will be controlled by this network. Researchers intend to extend their model to the dynamic control of legs with more than two degrees of freedom by using data on the control of multisegmented insect legs. Another immediate application of this neural control approach is also exhibited in biology: the escape reflex. Advanced robots are being equipped with tactile sensing and machine vision so that the sensory inputs to the robot controller are vast and complex. Neural networks are ideal for a lower level safety reflex controller because of their extremely fast response time. The combination of robotics, computer modeling, and neurobiology has been remarkably fruitful, and is likely to lead to deeper insights into the problems of real time sensorimotor control.

  2. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    SciTech Connect

    Kmet', Tibor; Kmet'ova, Maria

    2009-09-09

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  3. Neural controller for adaptive movements with unforeseen payloads

    NASA Technical Reports Server (NTRS)

    Kuperstein, Michael; Wang, Jyhpyng

    1990-01-01

    A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3 percent of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

  4. On neural networks in identification and control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Hyland, David C.

    1993-01-01

    This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

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

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    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.

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

    SciTech Connect

    Patro, S.; Kolarik, W.J.

    1999-06-01

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

  7. Evolving artificial neural networks to control chaotic systems

    NASA Astrophysics Data System (ADS)

    Weeks, Eric R.; Burgess, John M.

    1997-08-01

    We develop a genetic algorithm that produces neural network feedback controllers for chaotic systems. The algorithm was tested on the logistic and Hénon maps, for which it stabilizes an unstable fixed point using small perturbations, even in the presence of significant noise. The network training method [D. E. Moriarty and R. Miikkulainen, Mach. Learn. 22, 11 (1996)] requires no previous knowledge about the system to be controlled, including the dimensionality of the system and the location of unstable fixed points. This is the first dimension-independent algorithm that produces neural network controllers using time-series data. A software implementation of this algorithm is available via the World Wide Web.

  8. Neural network based dynamic controllers for industrial robots.

    PubMed

    Oh, S Y; Shin, W C; Kim, H G

    1995-09-01

    The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

  9. Proliferation control in neural stem and progenitor cells

    PubMed Central

    Homem, Catarina CF; Repic, Marko; Knoblich, Juergen A

    2015-01-01

    Neural circuit function can be drastically affected by variations in the number of cells that are produced during development or by a reduction in adult cell number due to disease. Unlike many other organs, the brain is unable to compensate for such changes by increasing cell numbers or altering the size of the cells. For this reason, unique cell cycle and cell growth control mechanisms operate in the developing and adult brain. In Drosophila melanogaster and mammalian neural stem and progenitor cells these mechanisms are intricately coordinated with the developmental age and the nutritional, metabolic and hormonal state of the animal. Defects in neural stem cell proliferation that result in the generation of incorrect cell numbers or defects in neural stem cell differentiation can cause microcephaly or megalencephaly. PMID:26420377

  10. Neural network guided search control in partial order planning

    SciTech Connect

    Zimmerman, T.

    1996-12-31

    The development of efficient search control methods is an active research topic in the field of planning. Investigation of a planning program integrated with a neural network (NN) that assists in search control is underway, and has produced promising preliminary results.

  11. Steam turbine stress control using NARX neural network

    NASA Astrophysics Data System (ADS)

    Dominiczak, Krzysztof; Rzadkowski, Romuald; Radulski, Wojciech

    2015-11-01

    Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks,whichwere trained based on experimentally validated FE model allows to control stresses in protected thickwalled steam turbine element with FE model quality. Additionally NARX neural network, which were trained base on FE model, includes: nonlinearity of steam expansion in turbine steam path during transients, nonlinearity of heat exchange inside the turbine during transients and nonlinearity of material properties during transients. In this article NARX neural networks stress controls is shown as an example of HP rotor of 18K390 turbine. HP part thermodynamic model as well as heat exchange model in vicinity of HP rotor,whichwere used in FE model of the HP rotor and the HP rotor FE model itself were validated based on experimental data for real turbine transient events. In such a way it is ensured that NARX neural network behave as real HP rotor during steam turbine transient events.

  12. Neural network application to aircraft control system design

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  13. Dissipative rendering and neural network control system design

    NASA Technical Reports Server (NTRS)

    Gonzalez, Oscar R.

    1995-01-01

    Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using

  14. Ideomotor feedback control in a recurrent neural network.

    PubMed

    Galtier, Mathieu

    2015-06-01

    The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

  15. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  16. Adaptive model predictive process control using neural networks

    DOEpatents

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  17. Generalized Predictive and Neural Generalized Predictive Control of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.

    2000-01-01

    The research work presented in this thesis addresses the problem of robust control of uncertain linear and nonlinear systems using Neural network-based Generalized Predictive Control (NGPC) methodology. A brief overview of predictive control and its comparison with Linear Quadratic (LQ) control is given to emphasize advantages and drawbacks of predictive control methods. It is shown that the Generalized Predictive Control (GPC) methodology overcomes the drawbacks associated with traditional LQ control as well as conventional predictive control methods. It is shown that in spite of the model-based nature of GPC it has good robustness properties being special case of receding horizon control. The conditions for choosing tuning parameters for GPC to ensure closed-loop stability are derived. A neural network-based GPC architecture is proposed for the control of linear and nonlinear uncertain systems. A methodology to account for parametric uncertainty in the system is proposed using on-line training capability of multi-layer neural network. Several simulation examples and results from real-time experiments are given to demonstrate the effectiveness of the proposed methodology.

  18. Self-teaching neural network learns difficult reactor control problem

    SciTech Connect

    Jouse, W.C.

    1989-01-01

    A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

  19. Helicopter trimming and tracking control using direct neural dynamic programming.

    PubMed

    Enns, R; Si, Jennie

    2003-01-01

    This paper advances a neural-network-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input multiple-output nonlinear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

  20. Adaptive neural-network-based control of robotic manipulators

    NASA Astrophysics Data System (ADS)

    Mitchell, Kyle; Dagli, Cihan H.

    2001-03-01

    Robotic manipulators are beginning to be seen doing more tasks in our environment. Classical controls engineers have long known how to control these automated hands. They have failed to address the continued control of these devices after parts of the control infrastructure have failed. A failed motor or actuator in a manipulator decreases its range of motion and changes its control structure. Most failures however do not render the manipulator useless. This paper will discuss the use of a neural network to actively update the controller design as portions of a manipulator fail. Actuators can become stuck and later free themselves. Motors can lose range of motion or stop completely. Connecting arms can become bent or entangled. Results will be presented on the ability to maintain functionality through a variety of failure modes. The neural network is constructed and tested in a Matlab environment. This allows testing of several neural network techniques such as back propagation and temporal processing without the need to continually reconfigure target hardware. In this paper we will demonstrate that a modified ensemble of back propagation experts can be trained to control a robotic manipulator without the need to calculate the inverse kinematics equations. Further individual experts can be retrained online to allow for adaptive control through changing dynamics. This allows for manipulators to remain in service through failures in the manipulator infrastructure without the need for human intervention into control equations.

  1. Neural Control Mechanisms and Body Fluid Homeostasis

    NASA Technical Reports Server (NTRS)

    Johnson, Alan Kim

    1998-01-01

    The goal of the proposed research was to study the nature of afferent signals to the brain that reflect the status of body fluid balance and to investigate the central neural mechanisms that process this information for the activation of response systems which restore body fluid homeostasis. That is, in the face of loss of fluids from intracellular or extracellular fluid compartments, animals seek and ingest water and ionic solutions (particularly Na(+) solutions) to restore the intracellular and extracellular spaces. Over recent years, our laboratory has generated a substantial body of information indicating that: (1) a fall in systemic arterial pressure facilitates the ingestion of rehydrating solutions and (2) that the actions of brain amine systems (e.g., norepinephrine; serotonin) are critical for precise correction of fluid losses. Because both acute and chronic dehydration are associated with physiological stresses, such as exercise and sustained exposure to microgravity, the present research will aid in achieving a better understanding of how vital information is handled by the nervous system for maintenance of the body's fluid matrix which is critical for health and well-being.

  2. Applications of neural networks to process control and modeling

    SciTech Connect

    Barnes, C.W.; Brown, S.K.; Flake, G.W.; Jones, R.D.; O'Rourke, M.K.; Lee, Y.C.

    1991-01-01

    Modeling and control of physical processes are universal parts of modern life, from control of chemical plants to riding a bicycle. Often, an effective model of the process is not known so that traditional control theory is of little use. If a process can be represented by a set of a data which captures it behavior over a range of parameter settings, a neural net can inductively model the process and form the basis of an optimization procedure. We present a neural network architecture which is particularly effective in process modeling and control. We discuss its effectiveness in several application areas as well as some of the non-ideal characteristics present in real control problems which effect the form and style of the network architecture and learning algorithm. 8 refs., 6 figs.

  3. An architecture for designing fuzzy logic controllers using neural networks

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  4. Backstepping Control Augmented by Neural Networks For Robot Manipulators

    NASA Astrophysics Data System (ADS)

    Belkheiri, Mohammed; Boudjema, Farès

    2008-06-01

    A new control approach is proposed to address the tracking problem of robot manipulators. In this approach, one relies first on a partially known model to the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modeling errors. The proposed approach is systematic, and exploits the known nonlinear dynamics to derive the stepwise virtual stabilizing control laws. At the final step, an augmented Lyapunov function is introduced to derive the adaptation laws of the network weights. The effectiveness of the proposed controller is demonstrated through computer simulation on PUMA 560 robot.

  5. Defining a neural network controller structure for a rubbertuator robot.

    PubMed

    Ozkan, M; Inoue, K; Negishi, K; Yamanaka, T

    2000-01-01

    Rubbertuator (Rubber-Actuator) robot arm is a pneumatic robot, unique with its lightweight, high power, compliant and spark free nature. Compressibility of air in the actuator tubes and the elastic nature of the rubber, however, are the two major sources of increased non-linearity and complexity in motion control. Soft computing, exploiting the tolerance of uncertainty and vagueness in cognitive reasoning has been offering easy to handle, robust, and low-priced solutions to several non-linear industrial applications. Nonetheless, the black-box approach in these systems results in application specific architectures with some important design parameters left for fine tuning (i.e. number of nodes in a neural network). In this study we propose a more systematic method in defining the structure of a soft computing technique, namely the backpropagation neural network, when used as a controller for rubbertuator robot systems. The structure of the neural network is based on the physical model of the robot, while the neural network itself is trained to learn the trajectory independent parameters of the model that are essential for defining the robot dynamics. The proposed system performance was compared with a well-tuned PID controller and shown to be more accurate in trajectory control for rubbertuator robots.

  6. Robust Neural Sliding Mode Control of Robot Manipulators

    SciTech Connect

    Nguyen Tran Hiep; Pham Thuong Cat

    2009-03-05

    This paper proposes a robust neural sliding mode control method for robot tracking problem to overcome the noises and large uncertainties in robot dynamics. The Lyapunov direct method has been used to prove the stability of the overall system. Simulation results are given to illustrate the applicability of the proposed method.

  7. Increasing autonomy of precision spacecraft using neural network adaptive control

    NASA Astrophysics Data System (ADS)

    Denoyer, Keith K.; Ninneman, R. Rory

    1999-01-01

    In recent years, there has been a significant interest in the use of adaptive methods for controlling structures in high precision aerospace applications. This is because adaptive methods offer the potential to autonomously adjust to system characteristics different from those modeled or seen in qualification testing. This is especially true of spacecraft, which are generally tested in a 1-g environment. Despite extensive research, it remains extremely difficult to predict on-orbit 0-g behavior. In addition, system dynamics often tend to be time varying. This can take the form of slow changes due to degradation of materials and aging of the spacecraft or sudden failures such as the loss of a sensor or actuator. These events become increasingly likely as spacecraft become more and more complex. By decreasing modeling and testing requirements, lowering operations and maintenance activities that require human intervention, and increasing reliability, adaptive methods have the potential to significantly reduce cost and increase performance of these systems. One class of adaptive control methods are those which utilize artificial neural networks. The use of neural networks has become increasingly mature in a number of areas such as image processing and speech recognition. However, despite a number of publications on the subject, very few instances exist where neural networks have actually been used in control and in particular, structural control applications. The United States Air Force Research Laboratory (AFRL) is currently engaged in advancing adaptive neural control technologies for application to precision space systems. This paper gives an overview of several past and current ground and space based adaptive neural control experiments.

  8. Active control of vibration using a neural network.

    PubMed

    Snyder, S D; Tanaka, N

    1995-01-01

    Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.

  9. Neural dynamic programming applied to rotorcraft flight control and reconfiguration

    NASA Astrophysics Data System (ADS)

    Enns, Russell James

    This dissertation introduces a new rotorcraft flight control methodology based on a relatively new form of neural control, neural dynamic programming (NDP). NDP is an on-line learning control scheme that is in its infancy and has only been applied to simple systems, such as those possessing a single control and a handful of states. This dissertation builds on the existing NDP concept to provide a comprehensive control system framework that can perform well as a learning controller for more realistic and practical systems of higher dimension such as helicopters. To accommodate such complex systems, the dissertation introduces the concept of a trim network that is seamlessly integrated into the NDP control structure and is also trained using this structure. This is the first time that neural networks have been applied to the helicopter control problem as a direct form of control without using other controller methodologies to augment the neural controller and without using order reducing simplifications such as axes decoupling. The dissertation focuses on providing a viable alternative helicopter control system design approach rather than providing extensive comparisons among various available controllers. As such, results showing the system's ability to stabilize the helicopter and to perform command tracking, without explicit comparison to other methods, are presented. In this research, design robustness was addressed by performing simulations under various disturbance conditions. All designs were tested using FLYRT, a sophisticated, industrial-scale, nonlinear, validated model of the Apache helicopter. Though illustrated for helicopters, the NDP control system framework should be applicable to general purpose multi-input multi-output (MIMO) control. In addition, this dissertation tackles the helicopter reconfigurable flight control problem, finding control solutions when the aircraft, and in particular its control actuators, are damaged. Such solutions have

  10. Adaptive nonlinear control of missiles using neural networks

    NASA Astrophysics Data System (ADS)

    McFarland, Michael Bryan

    Research has shown that neural networks can be used to improve upon approximate dynamic inversion for control of uncertain nonlinear systems. In one architecture, the neural network adaptively cancels inversion errors through on-line learning. Such learning is accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring stability of the closed-loop system. In this research, previous results using linear-in-parameters neural networks were reformulated in the context of a more general class of composite nonlinear systems, and the control scheme was shown to possess important similarities and major differences with established methods of adaptive control. The neural-adaptive nonlinear control methodology in question has been used to design an autopilot for an anti-air missile with enhanced agile maneuvering capability, and simulation results indicate that this approach is a feasible one. There are, however, certain difficulties associated with choosing the proper network architecture which make it difficult to achieve the rapid learning required in this application. Accordingly, this technique has been further extended to incorporate the important class of feedforward neural networks with a single hidden layer. These neural networks feature well-known approximation capabilities and provide an effective, although nonlinear, parameterization of the adaptive control problem. Numerical results from a six-degree-of-freedom nonlinear agile anti-air missile simulation demonstrate the effectiveness of the autopilot design based on multilayer networks. Previous work in this area has implicitly assumed precise knowledge of the plant order, and made no allowances for unmodeled dynamics. This thesis describes an approach to the problem of controlling a class of nonlinear systems in the face of both unknown nonlinearities and unmodeled dynamics. The proposed methodology is similar to robust adaptive control techniques derived for control of linear

  11. Internal models for interpreting neural population activity during sensorimotor control.

    PubMed

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

  12. Neural aspects of second language representation and language control.

    PubMed

    Abutalebi, Jubin

    2008-07-01

    A basic issue in the neurosciences of language is whether an L2 can be processed through the same neural mechanism underlying L1 acquisition and processing. In the present paper I review data from functional neuroimaging studies focusing on grammatical and lexico-semantic processing in bilinguals. The available evidence indicates that the L2 seems to be acquired through the same neural structures responsible for L1 acquisition. This fact is also observed for grammar acquisition in late L2 learners contrary to what one may expect from critical period accounts. However, neural differences for an L2 may be observed, in terms of more extended activity of the neural system mediating L1 processing. These differences may disappear once a more 'native-like' proficiency is established, reflecting a change in language processing mechanisms: from controlled processing for a weak L2 system (i.e., a less proficient L2) to more automatic processing. The neuroimaging data reviewed in this paper also support the notion that language control is a crucial aspect specific to the bilingual language system. The activity of brain areas related to cognitive control during the processing of a 'weak' L2 may reflect competition and conflict between languages which may be resolved with the intervention of these areas.

  13. Neural aspects of second language representation and language control.

    PubMed

    Abutalebi, Jubin

    2008-07-01

    A basic issue in the neurosciences of language is whether an L2 can be processed through the same neural mechanism underlying L1 acquisition and processing. In the present paper I review data from functional neuroimaging studies focusing on grammatical and lexico-semantic processing in bilinguals. The available evidence indicates that the L2 seems to be acquired through the same neural structures responsible for L1 acquisition. This fact is also observed for grammar acquisition in late L2 learners contrary to what one may expect from critical period accounts. However, neural differences for an L2 may be observed, in terms of more extended activity of the neural system mediating L1 processing. These differences may disappear once a more 'native-like' proficiency is established, reflecting a change in language processing mechanisms: from controlled processing for a weak L2 system (i.e., a less proficient L2) to more automatic processing. The neuroimaging data reviewed in this paper also support the notion that language control is a crucial aspect specific to the bilingual language system. The activity of brain areas related to cognitive control during the processing of a 'weak' L2 may reflect competition and conflict between languages which may be resolved with the intervention of these areas. PMID:18479667

  14. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  15. Variable Neural Adaptive Robust Control: A Switched System Approach

    SciTech Connect

    Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  16. Neural Generalized Predictive Control: A Newton-Raphson Implementation

    NASA Technical Reports Server (NTRS)

    Soloway, Donald; Haley, Pamela J.

    1997-01-01

    An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

  17. Asymmetric Distribution of GFAP in Glioma Multipotent Cells

    PubMed Central

    Guichet, Pierre-Olivier; Guelfi, Sophie; Ripoll, Chantal; Teigell, Marisa; Sabourin, Jean-Charles; Bauchet, Luc; Rigau, Valérie; Rothhut, Bernard; Hugnot, Jean-Philippe

    2016-01-01

    Asymmetric division (AD) is a fundamental mechanism whereby unequal inheritance of various cellular compounds during mitosis generates unequal fate in the two daughter cells. Unequal repartitions of transcription factors, receptors as well as mRNA have been abundantly described in AD. In contrast, the involvement of intermediate filaments in this process is still largely unknown. AD occurs in stem cells during development but was also recently observed in cancer stem cells. Here, we demonstrate the asymmetric distribution of the main astrocytic intermediate filament, namely the glial fibrillary acid protein (GFAP), in mitotic glioma multipotent cells isolated from glioblastoma (GBM), the most frequent type of brain tumor. Unequal mitotic repartition of GFAP was also observed in mice non-tumoral neural stem cells indicating that this process occurs across species and is not restricted to cancerous cells. Immunofluorescence and videomicroscopy were used to capture these rare and transient events. Considering the role of intermediate filaments in cytoplasm organization and cell signaling, we propose that asymmetric distribution of GFAP could possibly participate in the regulation of normal and cancerous neural stem cell fate. PMID:26953813

  18. Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton.

    PubMed

    Yu, Wen; Rosen, Jacob

    2013-04-01

    In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed. PMID:23033432

  19. Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton.

    PubMed

    Yu, Wen; Rosen, Jacob

    2013-04-01

    In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed.

  20. Computation and control with neural nets

    SciTech Connect

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  1. Contact inhibition of locomotion in vivo controls neural crest directional migration.

    PubMed

    Carmona-Fontaine, Carlos; Matthews, Helen K; Kuriyama, Sei; Moreno, Mauricio; Dunn, Graham A; Parsons, Maddy; Stern, Claudio D; Mayor, Roberto

    2008-12-18

    Contact inhibition of locomotion was discovered by Abercrombie more than 50 years ago and describes the behaviour of fibroblast cells confronting each other in vitro, where they retract their protrusions and change direction on contact. Its failure was suggested to contribute to malignant invasion. However, the molecular basis of contact inhibition of locomotion and whether it also occurs in vivo are still unknown. Here we show that neural crest cells, a highly migratory and multipotent embryonic cell population, whose behaviour has been likened to malignant invasion, demonstrate contact inhibition of locomotion both in vivo and in vitro, and that this accounts for their directional migration. When two migrating neural crest cells meet, they stop, collapse their protrusions and change direction. In contrast, when a neural crest cell meets another cell type, it fails to display contact inhibition of locomotion; instead, it invades the other tissue, in the same manner as metastatic cancer cells. We show that inhibition of non-canonical Wnt signalling abolishes both contact inhibition of locomotion and the directionality of neural crest migration. Wnt-signalling members localize at the site of cell contact, leading to activation of RhoA in this region. These results provide the first example of contact inhibition of locomotion in vivo, provide an explanation for coherent directional migration of groups of cells and establish a previously unknown role for non-canonical Wnt signalling.

  2. Neural changes in control implementation of a continuous task.

    PubMed

    Lungu, Ovidiu V; Binenstock, Meagan M; Pline, Megan A; Yeaton, Jennifer R; Carey, James R

    2007-03-14

    It is commonly agreed that control implementation, being a resource-consuming endeavor, is not exerted continuously or in simple tasks. However, most research in the field was done using tasks that varied the need for control on a trial-by-trial basis (e.g., Stroop, flanker) in a discrete manner. In this case, the anterior cingulate cortex (ACC) was found to monitor the need for control, whereas regions in the prefrontal cortex (PFC) were found to be involved in control implementation. Whether or not the same control mechanism would be used in continuous tasks was an open question. In our study, we found that in a continuous task, the same neural substrate subserves control monitoring (ACC) but that the neural substrate of control implementation changes over time. Early in the task, regions in the PFC were involved in control implementation, whereas later the control was taken over by subcortical structures, specifically the caudate. Our results suggest that humans possess a flexible control mechanism, with a specific structure dedicated to monitoring the need for control and with multiple structures involved in control implementation.

  3. Optogenetic feedback control of neural activity

    PubMed Central

    Newman, Jonathan P; Fong, Ming-fai; Millard, Daniel C; Whitmire, Clarissa J; Stanley, Garrett B; Potter, Steve M

    2015-01-01

    Optogenetic techniques enable precise excitation and inhibition of firing in specified neuronal populations and artifact-free recording of firing activity. Several studies have suggested that optical stimulation provides the precision and dynamic range requisite for closed-loop neuronal control, but no approach yet permits feedback control of neuronal firing. Here we present the ‘optoclamp’, a feedback control technology that provides continuous, real-time adjustments of bidirectional optical stimulation in order to lock spiking activity at specified targets over timescales ranging from seconds to days. We demonstrate how this system can be used to decouple neuronal firing levels from ongoing changes in network excitability due to multi-hour periods of glutamatergic or GABAergic neurotransmission blockade in vitro as well as impinging vibrissal sensory drive in vivo. This technology enables continuous, precise optical control of firing in neuronal populations in order to disentangle causally related variables of circuit activation in a physiologically and ethologically relevant manner. DOI: http://dx.doi.org/10.7554/eLife.07192.001 PMID:26140329

  4. DCS-Neural-Network Program for Aircraft Control and Testing

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    2006-01-01

    A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

  5. Controlling neural activity in Caenorhabditis elegans to evoke chemotactic behavior

    NASA Astrophysics Data System (ADS)

    Kocabas, Askin; Shen, Ching-Han; Guo, Zengcai V.; Ramanathan, Sharad

    2013-03-01

    Animals locate and track chemoattractive gradients in the environment to find food. With its simple nervous system, Caenorhabditis elegans is a good model system in which to understand how the dynamics of neural activity control this search behavior. To understand how the activity in its interneurons coordinate different motor programs to lead the animal to food, here we used optogenetics and new optical tools to manipulate neural activity directly in freely moving animals to evoke chemotactic behavior. By deducing the classes of activity patterns triggered during chemotaxis and exciting individual neurons with these patterns, we identified interneurons that control the essential locomotory programs for this behavior. Notably, we discovered that controlling the dynamics of activity in just one interneuron pair was sufficient to force the animal to locate, turn towards and track virtual light gradients.

  6. Neural Network Control of a Magnetically Suspended Rotor System

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin B.

    1998-01-01

    Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

  7. Neural control of vascular reactions: impact of emotion and attention.

    PubMed

    Okon-Singer, Hadas; Mehnert, Jan; Hoyer, Jana; Hellrung, Lydia; Schaare, Herma Lina; Dukart, Juergen; Villringer, Arno

    2014-03-19

    This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

  8. Noise Control for a Moving Evaluation Point Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Maeda, Toshiki; Shiraishi, Toshihiko

    2016-09-01

    This paper describes the noise control for a moving evaluation point using neural networks by making the best use of its learning ability. Noise control is a technology which is effective on low-frequency noise. Based on the principle of superposition, a primary sound wave can be cancelled at an evaluation point by emitting a secondary opposite sound wave. To obtain good control performance, it is important to precisely identify the characteristics of all the sound paths. One of the most popular algorithms of noise control is filtered-x LMS algorithm. This algorithm can deliver a good result while all the sound paths do not change. However, the control system becomes uncontrollable while the evaluation point is moving. To solve the problem, the characteristics of all the paths are must be identified at all time. In this paper, we applied neural networks with the learning ability to the noise control system to follow the time-varying paths and verified its control performance by numerical simulations. Then, dropout technique for the networks is also applied. Dropout is a technique that prevent the network from overfitting and enables better control performance. By applying dropout for noise control, it prevents the system from diverging.

  9. Sinusoidal modulation control method in a chaotic neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Qihanyue; Xie, Xiaoping; Zhu, Ping; Chen, Hongping; He, Guoguang

    2014-08-01

    Chaotic neural networks (CNNs) have chaotic dynamic associative memory properties: The memory states appear non-periodically, and cannot be converged to a stored pattern. Thus, it is necessary to control chaos in a CNN in order to recognize associative memory. In this paper, a novel control method, the sinusoidal modulation control method, has been proposed to control chaos in a CNN. In this method, a sinusoidal wave simplified from brain waves is used as a control signal to modulate a parameter of the CNN. The simulation results demonstrate the effectiveness of this control method. The controlled CNN can be applied to information processing. Moreover, the method provides a way to associate brain waves by controlling CNNs.

  10. Neural Evidence for the Flexible Control of Mental Representations.

    PubMed

    Lewis-Peacock, Jarrod A; Drysdale, Andrew T; Postle, Bradley R

    2015-10-01

    This study was designed to explore neural evidence for the simultaneous engagement of multiple mental codes while retaining a visual object in short-term memory (STM) and, if successful, to explore the neural bases of strategic prioritization among these codes. We used multivariate pattern analysis of fMRI data to track patterns of brain activity associated with three common mental codes: visual, verbal, and semantic. When participants did not know which dimension of a sample stimulus would be tested, patterns of brain activity during the memory delay indicated that a visual representation was quickly augmented with both verbal and semantic re-representations of the stimulus. The verbal code emerged as most highly activated, consistent with a canonical visual-to-phonological recoding operation in STM. If participants knew which dimension of a sample stimulus would be tested, brain activity patterns were biased toward the probe-relevant stimulus dimension. Interestingly, probe-irrelevant neural states persisted at an intermediate level of activation when they were potentially relevant later in the trial, but dropped to baseline when cued to be irrelevant. These results reveal the neural dynamics underlying the creation and retention of mental codes, and they illustrate the flexible control that humans can exert over these representations. PMID:24935778

  11. Prediction and control of neural responses to pulsatile electrical stimulation

    NASA Astrophysics Data System (ADS)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  12. Neural rotational speed control for wave energy converters

    NASA Astrophysics Data System (ADS)

    Amundarain, M.; Alberdi, M.; Garrido, A. J.; Garrido, I.

    2011-02-01

    Among the benefits arising from an increasing use of renewable energy are: enhanced security of energy supply, stimulation of economic growth, job creation and protection of the environment. In this context, this study analyses the performance of an oscillating water column device for wave energy conversion in function of the stalling behaviour in Wells turbines, one of the most widely used turbines in wave energy plants. For this purpose, a model of neural rotational speed control system is presented, simulated and implemented. This scheme is employed to appropriately adapt the speed of the doubly-fed induction generator coupled to the turbine according to the pressure drop entry, so as to avoid the undesired stalling behaviour. It is demonstrated that the proposed neural rotational speed control design adequately matches the desired relationship between the slip of the doubly-fed induction generator and the pressure drop input, improving the power generated by the turbine generator module.

  13. Neural mechanisms underlying auditory feedback control of speech.

    PubMed

    Tourville, Jason A; Reilly, Kevin J; Guenther, Frank H

    2008-02-01

    The neural substrates underlying auditory feedback control of speech were investigated using a combination of functional magnetic resonance imaging (fMRI) and computational modeling. Neural responses were measured while subjects spoke monosyllabic words under two conditions: (i) normal auditory feedback of their speech and (ii) auditory feedback in which the first formant frequency of their speech was unexpectedly shifted in real time. Acoustic measurements showed compensation to the shift within approximately 136 ms of onset. Neuroimaging revealed increased activity in bilateral superior temporal cortex during shifted feedback, indicative of neurons coding mismatches between expected and actual auditory signals, as well as right prefrontal and Rolandic cortical activity. Structural equation modeling revealed increased influence of bilateral auditory cortical areas on right frontal areas during shifted speech, indicating that projections from auditory error cells in posterior superior temporal cortex to motor correction cells in right frontal cortex mediate auditory feedback control of speech.

  14. Neural substrates linking balance control and anxiety

    NASA Technical Reports Server (NTRS)

    Balaban, Carey D.

    2002-01-01

    This communication provides an update of our understanding of the neurological bases for the close association between balance control and anxiety. New data suggest that a vestibulo-recipient region of the parabrachial nucleus (PBN) contains cells that respond to body rotation and position relative to gravity. The PBN, with its reciprocal relationships with the extended central amygdaloid nucleus, infralimbic cortex, and hypothalamus, appears to be an important node in a primary network that processes convergent vestibular, somatic, and visceral information processing to mediate avoidance conditioning, anxiety, and conditioned fear responses. Noradrenergic and serotonergic projections to the vestibular nuclei also have parallel connections with anxiety pathways. The coeruleo-vestibular pathway originates in caudal locus coeruleus (LC) and provides regionally specialized noradrenergic input to the vestibular nuclei, which likely mediate effects of alerting and vigilance on the sensitivity of vestibulo-motor circuits. Both serotonergic and nonserotonergic pathways from the dorsal raphe nucleus and the nucleus raphe obscurus also project differentially to the vestibular nuclei, and 5-HT(2A) receptors are expressed in amygdaloid and cortical targets of the PBN. It is proposed that the dorsal raphe nucleus pathway contributes to both (a) a tradeoff between motor and sensory (information gathering) aspects of responses to self-motion and (b) a calibration of the sensitivity of affective responses to aversive aspects of motion. This updated neurologic model continues to be a synthetic schema for investigating the neurological and neurochemical bases for comorbidity of balance disorders and anxiety disorders.

  15. Neural control of chronic stress adaptation

    PubMed Central

    Herman, James P.

    2013-01-01

    Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA) axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process. PMID:23964212

  16. Color control using neural networks and its application

    NASA Astrophysics Data System (ADS)

    Tominaga, Shoji

    1996-03-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of neural network. The CIE-L*a*b* color system is used as the color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller finds the CMYK signals necessary to produce the desired L*a*b* values from a printer. Our solution method for this control is based on a two-phase procedure. Validity of our method is shown in an experiment using a dye sublimation printer.

  17. Neural Control Adaptation to Motor Noise Manipulation.

    PubMed

    Hasson, Christopher J; Gelina, Olga; Woo, Garrett

    2016-01-01

    Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability. PMID:26973487

  18. Neural Control Adaptation to Motor Noise Manipulation

    PubMed Central

    Hasson, Christopher J.; Gelina, Olga; Woo, Garrett

    2016-01-01

    Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability. PMID:26973487

  19. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  20. Chaotic neural network applied to two-dimensional motion control.

    PubMed

    Yoshida, Hiroyuki; Kurata, Shuhei; Li, Yongtao; Nara, Shigetoshi

    2010-03-01

    Chaotic dynamics generated in a chaotic neural network model are applied to 2-dimensional (2-D) motion control. The change of position of a moving object in each control time step is determined by a motion function which is calculated from the firing activity of the chaotic neural network. Prototype attractors which correspond to simple motions of the object toward four directions in 2-D space are embedded in the neural network model by designing synaptic connection strengths. Chaotic dynamics introduced by changing system parameters sample intermediate points in the high-dimensional state space between the embedded attractors, resulting in motion in various directions. By means of adaptive switching of the system parameters between a chaotic regime and an attractor regime, the object is able to reach a target in a 2-D maze. In computer experiments, the success rate of this method over many trials not only shows better performance than that of stochastic random pattern generators but also shows that chaotic dynamics can be useful for realizing robust, adaptive and complex control function with simple rules.

  1. An integrated architecture of adaptive neural network control for dynamic systems

    SciTech Connect

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

  2. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  3. Combustion control of municipal incinerators by fuzzy neural network logic

    SciTech Connect

    Chang, N.B.; Chang, Y.H.

    1996-12-31

    The successful operation of mass burn waterwall incinerators involves many uncertain factors. Not only the physical composition and chemical properties of the refuse but also the complexity of combustion mechanism would significantly influence the performance of waste treatment. Due to the rising concerns of dioxin/furan emissions from municipal incinerators, improved combustion control algorithms, such as fuzzy and its fusion control technologies, have gradually received attention in the scientific community. This paper describes a fuzzy and neural network control logic for the refuse combustion process in a mass burn waterwall incinerator. It is anticipated that this system can also be easily applied to several other types of municipal incinerators, such as modular, rotary kiln, RDF and fluidized bed incinerators, by slightly modified steps. Partial performance of this designed controller is tested by computer simulation using identified process model in this analysis. Process control could be sensitive especially for the control of toxic substance emissions, such as dioxin and furans.

  4. Neural correlates of cognitive style and flexible cognitive control.

    PubMed

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style.

  5. Neural network learning of optimal Kalman prediction and control.

    PubMed

    Linsker, Ralph

    2008-11-01

    Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw inferences for biological function. It has been suggested that the local cortical circuit (LCC) architecture may perform core functions (as yet unknown) that underlie sensory, motor, and other cortical processing. It is reasonable to conjecture that such functions may include prediction, the estimation or inference of missing or noisy sensory data, and the goal-driven generation of control signals. The resemblances found between the KPC NN architecture and that of the LCC are consistent with this conjecture.

  6. Neural correlates of cognitive style and flexible cognitive control.

    PubMed

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style. PMID:25812714

  7. Flexible neural mechanisms of cognitive control within human prefrontal cortex.

    PubMed

    Braver, Todd S; Paxton, Jessica L; Locke, Hannah S; Barch, Deanna M

    2009-05-01

    A major challenge in research on executive control is to reveal its functional decomposition into underlying neural mechanisms. A typical assumption is that this decomposition occurs solely through anatomically based dissociations. Here we tested an alternative hypothesis that different cognitive control processes may be implemented within the same brain regions, with fractionation and dissociation occurring on the basis of temporal dynamics. Regions within lateral prefrontal cortex (PFC) were examined that, in a prior study, exhibited contrasting temporal dynamics between older and younger adults during performance of the AX-CPT cognitive control task. The temporal dynamics in younger adults fit a proactive control pattern (primarily cue-based activation), whereas in older adults a reactive control pattern was found (primarily probe-based activation). In the current study, we found that following a period of task-strategy training, these older adults exhibited a proactive shift within a subset of the PFC regions, normalizing their activity dynamics toward young adult patterns. Conversely, under conditions of penalty-based monetary incentives, the younger adults exhibited a reactive shift some of the same regions, altering their temporal dynamics toward the older adult baseline pattern. These experimentally induced crossover patterns of temporal dynamics provide strong support for dual modes of cognitive control that can be flexibly shifted within PFC regions, via modulation of neural responses to changing task conditions or behavioral goals. PMID:19380750

  8. Light, heat, action: neural control of fruit fly behaviour

    PubMed Central

    Owald, David; Lin, Suewei; Waddell, Scott

    2015-01-01

    The fruit fly Drosophila melanogaster has emerged as a popular model to investigate fundamental principles of neural circuit operation. The sophisticated genetics and small brain permit a cellular resolution understanding of innate and learned behavioural processes. Relatively recent genetic and technical advances provide the means to specifically and reproducibly manipulate the function of many fly neurons with temporal resolution. The same cellular precision can also be exploited to express genetically encoded reporters of neural activity and cell-signalling pathways. Combining these approaches in living behaving animals has great potential to generate a holistic view of behavioural control that transcends the usual molecular, cellular and systems boundaries. In this review, we discuss these approaches with particular emphasis on the pioneering studies and those involving learning and memory. PMID:26240426

  9. Oviposition digging in the grasshopper. II. Descending neural control.

    PubMed

    Thompson, K J

    1986-05-01

    Transection of the ventral nerve cord of female grasshoppers activates the rhythmical motor programme for oviposition digging. Electrical stimulation of the cut nerve cord had the following effects on elicited oviposition motor activity: short- and long-lasting inhibition of activity, phase resetting and modulation of burst frequency. Cold saline applied to the nerve cord reversibly elicited the oviposition motor programme. The effects of transection and stimulation at different levels of the nerve cord indicate that the higher neural control of the motor pattern is not confined to the head ganglia, but includes a thoracic component. In intracellular recordings of ventral opener motoneurones, stimulus-related IPSPs were observed in response to stimulation of the cut nerve cord. Stimulation also abolished slow wave synaptic input to the motoneurones during inhibition of the oviposition motor programme. It is suggested that oviposition digging behaviour is initiated and maintained by a mechanism of 'release' from descending neural inhibition.

  10. Alcohol and behavioral control: cognitive and neural mechanisms.

    PubMed

    Vogel-Sprott, M; Easdon, C; Fillmore, M; Finn, P; Justus, A

    2001-01-01

    This article represents the proceedings of a symposium at the 2000 RSA Meeting in Denver, Colorado. The organizer/chair was Muriel Vogel-Sprott. The presentations were (1) Alcohol-induced impairment of inhibitory control: Some commonalities with attention deficit hyperactivity disorder, by Mark Fillmore; (2) Neural interactions that underlie response inhibition under alcohol: A functional magnetic resonance imaging investigation, by Craig Easdon; (3) Intentional control of behavior under alcohol, by Muriel Vogel-Sprott; and (4) Working memory and the disinhibiting effects of alcohol on passive avoidance learning, by Alicia Justius and Peter Finn.

  11. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

    Graham, D. P. W.; Deleuterio, G. M. T.

    1993-01-01

    An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

  12. Neural net controller for inlet pressure control of rocket engine testing

    NASA Technical Reports Server (NTRS)

    Trevino, Luis C.

    1994-01-01

    Many dynamic systems operate in select operating regions, each exhibiting characteristic modes of behavior. It is traditional to employ standard adjustable gain proportional-integral-derivative (PID) loops in such systems where no apriori model information is available. However, for controlling inlet pressure for rocket engine testing, problems in fine tuning, disturbance accommodation, and control gains for new profile operating regions (for research and development) are typically encountered. Because of the capability of capturing I/O peculiarities, using NETS, a back propagation trained neural network is specified. For select operating regions, the neural network controller is simulated to be as robust as the PID controller. For a comparative analysis, the higher order moment neural array (HOMNA) method is used to specify a second neural controller by extracting critical exemplars from the I/O data set. Furthermore, using the critical exemplars from the HOMNA method, a third neural controller is developed using NETS back propagation algorithm. All controllers are benchmarked against each other.

  13. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  14. Discrete neural dynamic programming in wheeled mobile robot control

    NASA Astrophysics Data System (ADS)

    Hendzel, Zenon; Szuster, Marcin

    2011-05-01

    In this paper we propose a discrete algorithm for a tracking control of a two-wheeled mobile robot (WMR), using an advanced Adaptive Critic Design (ACD). We used Dual-Heuristic Programming (DHP) algorithm, that consists of two parametric structures implemented as Neural Networks (NNs): an actor and a critic, both realized in a form of Random Vector Functional Link (RVFL) NNs. In the proposed algorithm the control system consists of the DHP adaptive critic, a PD controller and a supervisory term, derived from the Lyapunov stability theorem. The supervisory term guaranties a stable realization of a tracking movement in a learning phase of the adaptive critic structure and robustness in face of disturbances. The discrete tracking control algorithm works online, uses the WMR model for a state prediction and does not require a preliminary learning. Verification has been conducted to illustrate the performance of the proposed control algorithm, by a series of experiments on the WMR Pioneer 2-DX.

  15. Intelligent control based on fuzzy logic and neural net theory

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  16. Statistical process control using optimized neural networks: a case study.

    PubMed

    Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

    2014-09-01

    The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. PMID:24210290

  17. A reflexive neural network for dynamic biped walking control.

    PubMed

    Geng, Tao; Porr, Bernd; Wörgötter, Florentin

    2006-05-01

    Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincaré map analysis

  18. Differentiation of Multipotent Vascular Stem Cells Contributes to Vascular Diseases

    PubMed Central

    Tang, Zhenyu; Wang, Aijun; Yuan, Falei; Yan, Zhiqiang; Liu, Bo; Chu, Julia S.; Helms, Jill A.

    2012-01-01

    It is generally accepted that the de-differentiation of smooth muscle cells (SMCs) from contractile to proliferative/synthetic phenotype has an important role during vascular remodeling and diseases. Here we provide evidence that challenges this theory. We identify a new type of multipotent vascular stem cell (MVSC) in blood vessel wall. MVSCs express markers including Sox17, Sox10 and S100β, are cloneable, have telomerase activity, and can differentiate into neural cells and mesenchymal stem cell (MSC)-like cells that subsequently differentiate into SMCs. On the other hand, we use lineage tracing with smooth muscle myosin heavy chain as a marker to show that MVSCs and proliferative or synthetic SMCs do not arise from the de-differentiation of mature SMCs. Upon vascular injuries, MVSCs, instead of SMCs, become proliferative, and MVSCs can differentiate into SMCs and chondrogenic cells, thus contributing to vascular remodeling and neointimal hyperplasia. These findings support a new hypothesis that the differentiation of MVSCs, rather than the de-differentiation of SMCs, contributes to vascular remodeling and diseases. PMID:22673902

  19. [Multipotency of adult stem cells derived from human amnion].

    PubMed

    Shi, Mingxia; Li, Weijia; Li, Bingzong; Li, Jing; Zhao, Chunhua

    2009-05-01

    Adult stem cells are drawing more and more attention due to the potential application in degenerative medicine without posing any moral problem. There is growing evidence showing that the human amnion contains various types of adult stem cell. Since amniotic tissue is readily available, it has the potential to be an important source of regenerative medicine material. In this study we tried to find multipotent adult stem cells in human amnion. We isolated stem cells from amniotic mesenchymal cells by limiting dilution assay. Similar to bone marrow derived mesenchymal stem cells, these cells displayed a fibroblast like appearance. They were positive for CD105, CD29, CD44, negative for haematopoietic (GlyA, CD31, CD34, CD45) and epithelial cell (pan-CK) markers. These stem cells had the potential to differentiate not only into osteogenic, adipogenic and endothelial lineages, but also hepatocyte-like cells and neural cells at the single-cell level depending on the culture conditions. They had the capacity for self-renewal and multilineage differentiation even after being expanded for more than 30 population doublings in vitro. So they may be an ideal stem cell source for inherited or degenerative diseases treatment.

  20. Expansion of Multipotent Stem Cells from the Adult Human Brain

    PubMed Central

    Murrell, Wayne; Palmero, Emily; Bianco, John; Stangeland, Biljana; Joel, Mrinal; Paulson, Linda; Thiede, Bernd; Grieg, Zanina; Ramsnes, Ingunn; Skjellegrind, Håvard K.; Nygård, Ståle; Brandal, Petter; Sandberg, Cecilie; Vik-Mo, Einar; Palmero, Sheryl; Langmoen, Iver A.

    2013-01-01

    The discovery of stem cells in the adult human brain has revealed new possible scenarios for treatment of the sick or injured brain. Both clinical use of and preclinical research on human adult neural stem cells have, however, been seriously hampered by the fact that it has been impossible to passage these cells more than a very few times and with little expansion of cell numbers. Having explored a number of alternative culturing conditions we here present an efficient method for the establishment and propagation of human brain stem cells from whatever brain tissue samples we have tried. We describe virtually unlimited expansion of an authentic stem cell phenotype. Pluripotency proteins Sox2 and Oct4 are expressed without artificial induction. For the first time multipotency of adult human brain-derived stem cells is demonstrated beyond tissue boundaries. We characterize these cells in detail in vitro including microarray and proteomic approaches. Whilst clarification of these cells’ behavior is ongoing, results so far portend well for the future repair of tissues by transplantation of an adult patient’s own-derived stem cells. PMID:23967194

  1. Characterizing the radioresponse of pluripotent and multipotent human stem cells.

    PubMed

    Lan, Mary L; Acharya, Munjal M; Tran, Katherine K; Bahari-Kashani, Jessica; Patel, Neal H; Strnadel, Jan; Giedzinski, Erich; Limoli, Charles L

    2012-01-01

    The potential capability of stem cells to restore functionality to diseased or aged tissues has prompted a surge of research, but much work remains to elucidate the response of these cells to genotoxic agents. To more fully understand the impact of irradiation on different stem cell types, the present study has analyzed the radioresponse of human pluripotent and multipotent stem cells. Human embryonic stem (ES) cells, human induced pluripotent (iPS) cells, and iPS-derived human neural stem cells (iPS-hNSCs) cells were irradiated and analyzed for cell survival parameters, differentiation, DNA damage and repair and oxidative stress at various times after exposure. While irradiation led to dose-dependent reductions in survival, the fraction of surviving cells exhibited dose-dependent increases in metabolic activity. Irradiation did not preclude germ layer commitment of ES cells, but did promote neuronal differentiation. ES cells subjected to irradiation exhibited early apoptosis and inhibition of cell cycle progression, but otherwise showed normal repair of DNA double-strand breaks. Cells surviving irradiation also showed acute and persistent increases in reactive oxygen and nitrogen species that were significant at nearly all post-irradiation times analyzed. We suggest that stem cells alter their redox homeostasis to adapt to adverse conditions and that radiation-induced oxidative stress plays a role in regulating the function and fate of stem cells within tissues compromised by radiation injury.

  2. Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation

    NASA Astrophysics Data System (ADS)

    Kmet', Tibor; Kmet'ov, Mria

    2010-09-01

    A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.

  3. Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2007-01-01

    The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

  4. Internal models for interpreting neural population activity during sensorimotor control

    PubMed Central

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output. DOI: http://dx.doi.org/10.7554/eLife.10015.001 PMID:26646183

  5. Analog neural network control method proposed for use in a backup satellite control mode

    SciTech Connect

    Frigo, J.R.; Tilden, M.W.

    1998-03-01

    The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a model of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.

  6. Upper Torso Control for HOAP-2 Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Sandoval, Steven P.

    2005-01-01

    Humanoid robots have similar physical builds and motion patterns as humans. Not only does this provide a suitable operating environment for the humanoid but it also opens up many research doors on how humans function. The overall objective is replacing humans operating in unsafe environments. A first target application is assembly of structures for future lunar-planetary bases. The initial development platform is a Fujitsu HOAP-2 humanoid robot. The goal for the project is to demonstrate the capability of a HOAP-2 to autonomously construct a cubic frame using provided tubes and joints. This task will require the robot to identify several items, pick them up, transport them to the build location, then properly assemble the structure. The ability to grasp and assemble the pieces will require improved motor control and the addition of tactile feedback sensors. In recent years, learning-based control is becoming more and more popular; for implementing this method we will be using the Adaptive Neural Fuzzy Inference System (ANFIS). When using neural networks for control, no complex models of the system must be constructed in advance-only input/output relationships are required to model the system.

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

    PubMed

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

    2013-01-01

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

  8. Some historical reflections on the neural control of locomotion.

    PubMed

    Clarac, François

    2008-01-01

    Thought on the neural control of locomotion dates back to antiquity. In this article, however, the focus is more recent by starting with some major 17th century concepts, which were developed by René Descartes, a French philosopher; Thomas Willis, an English anatomist; and Giovanni Borelli, an Italian physiologist and physicist. Each relied on his personal expertise to theorize on the organization and control of movements. The 18th and early 19th centuries saw work on both the central and peripheral control of movement: the former most notably by Johann Unzer, Marie Jean-Pierre Flourens and Julien-Jean-César Legallois, and the latter by Unzer, Jirí Procháska and many others. Next in the 19th century, neurologists used human locomotion as a precise tool for characterizing motor pathologies: e.g., Guillaume Duchenne de Boulogne's description of locomotor ataxia. Jean-Martin Charcot considered motor control to be organized at two levels of the central nervous system: the cerebral cortex and the spinal cord. Maurice Philippson's defined the dog's step cycle and considered that locomotion used both central and reflex mechanisms. Charles Sherrington explained that locomotor control was usually thought to consist of a succession of peripheral reflexes (e.g., the stepping reflexes). Thomas Graham Brown's then contemporary evidence for the spinal origin of locomotor rhythmicity languished in obscurity until the early 1960s. By then the stage was set for an international assault on the neural control of locomotion, which featured research conducted on both invertebrate and vertebrate animal models. These contributions have progressively became more integrated and interactive, with current work emphasizing that locomotor control involves a seamless integration between central locomotor networks and peripheral feedback.

  9. Multipotent pancreas progenitors: Inconclusive but pivotal topic.

    PubMed

    Jiang, Fang-Xu; Morahan, Grant

    2015-12-26

    The establishment of multipotent pancreas progenitors (MPP) should have a significant impact not only on the ontology of the pancreas, but also for the translational research of glucose-responding endocrine β-cells. Deficiency of the latter may lead to the pandemic type 1 or type 2 diabetes mellitus, a metabolic disorder. An ideal treatment of which would potentially be the replacement of destroyed or failed β-cells, by restoring function of endogenous pancreatic endocrine cells or by transplantation of donor islets or in vitro generated insulin-secreting cells. Thus, considerable research efforts have been devoted to identify MPP candidates in the pre- and post-natal pancreas for the endogenous neogenesis or regeneration of endocrine insulin-secreting cells. In order to advance this inconclusive but critical field, we here review the emerging concepts, recent literature and newest developments of potential MPP and propose measures that would assist its forward progression. PMID:26730269

  10. Calcification of multipotent prostate tumor endothelium.

    PubMed

    Dudley, Andrew C; Khan, Zia A; Shih, Shou-Ching; Kang, Soo-Young; Zwaans, Bernadette M M; Bischoff, Joyce; Klagsbrun, Michael

    2008-09-01

    Solid tumors require new blood vessels for growth and metastasis, yet the biology of tumor-specific endothelial cells is poorly understood. We have isolated tumor endothelial cells from mice that spontaneously develop prostate tumors. Clonal populations of tumor endothelial cells expressed hematopoietic and mesenchymal stem cell markers and differentiated to form cartilage- and bone-like tissues. Chondrogenic differentiation was accompanied by an upregulation of cartilage-specific col2a1 and sox9, whereas osteocalcin and the metastasis marker osteopontin were upregulated during osteogenic differentiation. In human and mouse prostate tumors, ectopic vascular calcification was predominately luminal and colocalized with the endothelial marker CD31. Thus, prostate tumor endothelial cells are atypically multipotent and can undergo a mesenchymal-like transition.

  11. Neural network control of multifingered robot hands using visual feedback.

    PubMed

    Zhao, Yu; Cheah, Chien Chern

    2009-05-01

    It is interesting to observe that humans are able to manipulate an object easily and skillfully without the exact knowledge of the object, contact points, or kinematics of our fingers. However, research so far on multifingered robot control has assumed that the kinematics and contact points of the fingers are known exactly. In many applications of multifingered robot hands, the kinematics and contact points of the fingers are uncertain and structures of the Jacobian matrices are unknown. In this paper, we propose an adaptive neural network (NN) Jacobian controller for multifingered robot hand with uncertainties in kinematics, Jacobian matrices, and dynamics. It is shown that using NNs, the uniform ultimate boundedness of the position error can be achieved in the presence of the uncertainties. Simulation results are presented to illustrate the performance of the proposed controller.

  12. Neural mechanisms of impulse control in sexually risky adolescents

    PubMed Central

    Goldenberg, Diane; Telzer, Eva H.; Lieberman, Matthew D.; Fuligni, Andrew; Galván, Adriana

    2014-01-01

    The consequences of risky sexual behavior are of public concern. Adolescents contribute disproportionately to negative consequences of risky sexual behavior. However, no research has examined the neural correlates of impulse control and real-world engagement in risky sexual behavior in this population. The aim of the present study was to examine this question. Twenty sexually active adolescents performed an impulse control task during a functional magnetic resonance imaging (fMRI) scan and risky sexual behaviors were assessed through self-report. Sexual riskiness ratings were negatively associated with activation in the prefrontal cortex during response inhibition. These results suggest that diminished engagement of impulse control circuitry may contribute to sexual riskiness in adolescents. PMID:23835204

  13. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    NASA Astrophysics Data System (ADS)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  14. Optimized biogas-fermentation by neural network control.

    PubMed

    Holubar, P; Zani, L; Hager, M; Fröschl, W; Radak, Z; Braun, R

    2003-01-01

    In this work several feed-forward back-propagation neural networks (FFBP) were trained in order to model, and subsequently control, methane production in anaerobic digesters. To produce data for the training of the neural nets, four anaerobic continuous stirred tank reactors (CSTR) were operated in steady-state conditions at organic loading rates (Br) of about 2 kg x m(-3) x d(-1) chemical oxygen demand (COD), and disturbed by pulse-like increase of the organic loading rate. For the pulses additional carbon sources were added to the basic feed (surplus- and primary sludge) to simulate cofermentation and to increase the COD. Measured parameters were: gas composition, methane production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and COD of feed and effluent. A hierarchical system of neural nets was developed and embedded in a Decision Support System (DSS). A 3-3-1 FFBP simulated the pH with a regression coefficient of 0.82. A 9-3-3 FFBP simulated the volatile fatty acid concentration in the sludge with a regression coefficient of 0.86. And a 9-3-2 FFBP simulated the gas production and gas composition with a regression coefficient of 0.90 and 0.80 respectively. A lab-scale anaerobic CSTR controlled by this tool was able to maintain a methane concentration of about 60% at a rather high gas production rate of between 5 to 5.6 m3 x m(-3) x d(-1).

  15. Compound intelligent control system combining fuzzy control with neural networks in a permanent magnetic synchronous motor

    NASA Astrophysics Data System (ADS)

    Zhang, Zhiyuan; Li, Weili; Li, Taifu

    2005-12-01

    An AC motor belongs to the category of a controlled object that is multi-variable, nonlinear and strong correlation, complex to mathematical model, and whose control performance is affected by a time-changing parameter. Therefore, it is very difficult to obtain the desired static and dynamic characteristic through a general fixed regulator. In this paper, the authors present a compound intelligent control strategy, combined with a neural network and fuzzy control. Considering that a neural network is good at self-learning, and a single fuzzy control algorithm is rapid in its response characteristics, the compound control strategy can compensate for a disadvantage of fuzzy control, which is associated with poor stability and precision and also requires solving a puzzle in the time-changing parameters in the controlled object. On the basis of a dynamic model of the permanent magnetic synchronous motor and its working principle, the authors designed the block diagram of a control system, combined a neural PID control and fuzzy control, and studied the corresponding control algorithm in detail. The simulation results show that the compound intelligent control system is good in dynamic performance and robustness.

  16. The endocannabinoid system drives neural progenitor proliferation.

    PubMed

    Aguado, Tania; Monory, Krisztina; Palazuelos, Javier; Stella, Nephi; Cravatt, Benjamin; Lutz, Beat; Marsicano, Giovanni; Kokaia, Zaal; Guzmán, Manuel; Galve-Roperh, Ismael

    2005-10-01

    The discovery of multipotent neural progenitor (NP) cells has provided strong support for the existence of neurogenesis in the adult brain. However, the signals controlling NP proliferation remain elusive. Endocannabinoids, the endogenous counterparts of marijuana-derived cannabinoids, act as neuromodulators via presynaptic CB1 receptors and also control neural cell death and survival. Here we show that progenitor cells express a functional endocannabinoid system that actively regulates cell proliferation both in vitro and in vivo. Specifically, NPs produce endocannabinoids and express the CB1 receptor and the endocannabinoid-inactivating enzyme fatty acid amide hydrolase (FAAH). CB1 receptor activation promotes cell proliferation and neurosphere generation, an action that is abrogated in CB1-deficient NPs. Accordingly, proliferation of hippocampal NPs is increased in FAAH-deficient mice. Our results demonstrate that endocannabinoids constitute a new group of signaling cues that regulate NP proliferation and thus open novel therapeutic avenues for manipulation of NP cell fate in the adult brain.

  17. Autonomic neural control of dynamic cerebral autoregulation in humans

    NASA Technical Reports Server (NTRS)

    Zhang, Rong; Zuckerman, Julie H.; Iwasaki, Kenichi; Wilson, Thad E.; Crandall, Craig G.; Levine, Benjamin D.

    2002-01-01

    BACKGROUND: The purpose of the present study was to determine the role of autonomic neural control of dynamic cerebral autoregulation in humans. METHODS AND RESULTS: We measured arterial pressure and cerebral blood flow (CBF) velocity in 12 healthy subjects (aged 29+/-6 years) before and after ganglion blockade with trimethaphan. CBF velocity was measured in the middle cerebral artery using transcranial Doppler. The magnitude of spontaneous changes in mean blood pressure and CBF velocity were quantified by spectral analysis. The transfer function gain, phase, and coherence between these variables were estimated to quantify dynamic cerebral autoregulation. After ganglion blockade, systolic and pulse pressure decreased significantly by 13% and 26%, respectively. CBF velocity decreased by 6% (P<0.05). In the very low frequency range (0.02 to 0.07 Hz), mean blood pressure variability decreased significantly (by 82%), while CBF velocity variability persisted. Thus, transfer function gain increased by 81%. In addition, the phase lead of CBF velocity to arterial pressure diminished. These changes in transfer function gain and phase persisted despite restoration of arterial pressure by infusion of phenylephrine and normalization of mean blood pressure variability by oscillatory lower body negative pressure. CONCLUSIONS: These data suggest that dynamic cerebral autoregulation is altered by ganglion blockade. We speculate that autonomic neural control of the cerebral circulation is tonically active and likely plays a significant role in the regulation of beat-to-beat CBF in humans.

  18. Geometrical approach to neural net control of movements and posture

    NASA Technical Reports Server (NTRS)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  19. Geometrical approach to neural net control of movements and posture.

    PubMed

    Pellionisz, A J; Ramos, C F

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control. PMID:8234751

  20. Output feedback control of a quadrotor UAV using neural networks.

    PubMed

    Dierks, Travis; Jagannathan, Sarangapani

    2010-01-01

    In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

  1. Central neural control of thermoregulation and brown adipose tissue.

    PubMed

    Morrison, Shaun F

    2016-04-01

    Central neural circuits orchestrate the homeostatic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response. This review summarizes the experimental underpinnings of our current model of the CNS pathways controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction controlling heat loss, and shivering and brown adipose tissue for thermogenesis. The activation of these effectors is regulated by parallel but distinct, effector-specific, core efferent pathways within the CNS that share a common peripheral thermal sensory input. Via the lateral parabrachial nucleus, skin thermal afferent input reaches the hypothalamic preoptic area to inhibit warm-sensitive, inhibitory output neurons which control heat production by inhibiting thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to thermogenesis-controlling premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation of spinal circuits necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus sympathetic premotor neurons controlling cutaneous vasoconstriction. The model proposed for central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation and elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation. PMID:26924538

  2. Neural time course of emotional conflict control: an ERP study.

    PubMed

    Shen, Yimo; Xue, Song; Wang, Kangcheng; Qiu, Jiang

    2013-04-29

    Previous imaging studies have revealed brain mechanisms associated with emotional conflict control. However, the neural time course remains largely unknown. Therefore, in the present study a face-word Stroop task was used to explore the electrophysiological correlates of emotional conflict control by using event-related potentials (ERPs). Behavioral data indicated that response time of congruent condition was faster than incongruent condition, while the accuracy rates of congruent condition was higher than incongruent condition, which showed a robust emotional conflict effect. ERP revealed N350-550 and P700-800 components in the incongruent minus congruent condition. N350-550 might be related to conflict resolution and response selection; P700-800 might be related to post-response monitoring.

  3. Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

    ERIC Educational Resources Information Center

    Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

    2012-01-01

    Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

  4. Neural Conflict–Control Mechanisms Improve Memory for Target Stimuli

    PubMed Central

    Krebs, Ruth M.; Boehler, Carsten N.; De Belder, Maya; Egner, Tobias

    2015-01-01

    According to conflict-monitoring models, conflict serves as an internal signal for reinforcing top-down attention to task-relevant information. While evidence based on measures of ongoing task performance supports this idea, implications for long-term consequences, that is, memory, have not been tested yet. Here, we evaluated the prediction that conflict-triggered attentional enhancement of target-stimulus processing should be associated with superior subsequent memory for those stimuli. By combining functional magnetic resonance imaging (fMRI) with a novel variant of a face-word Stroop task that employed trial-unique face stimuli as targets, we were able to assess subsequent (incidental) memory for target faces as a function of whether a given face had previously been accompanied by congruent, neutral, or incongruent (conflicting) distracters. In line with our predictions, incongruent distracters not only induced behavioral conflict, but also gave rise to enhanced memory for target faces. Moreover, conflict-triggered neural activity in prefrontal and parietal regions was predictive of subsequent retrieval success, and displayed conflict-enhanced functional coupling with medial-temporal lobe regions. These data provide support for the proposal that conflict evokes enhanced top-down attention to task-relevant stimuli, thereby promoting their encoding into long-term memory. Our findings thus delineate the neural mechanisms of a novel link between cognitive control and memory. PMID:24108799

  5. Control of Neural Stem Cell Survival by Electroactive Polymer Substrates

    PubMed Central

    Lundin, Vanessa; Herland, Anna; Berggren, Magnus

    2011-01-01

    Stem cell function is regulated by intrinsic as well as microenvironmental factors, including chemical and mechanical signals. Conducting polymer-based cell culture substrates provide a powerful tool to control both chemical and physical stimuli sensed by stem cells. Here we show that polypyrrole (PPy), a commonly used conducting polymer, can be tailored to modulate survival and maintenance of rat fetal neural stem cells (NSCs). NSCs cultured on PPy substrates containing different counter ions, dodecylbenzenesulfonate (DBS), tosylate (TsO), perchlorate (ClO4) and chloride (Cl), showed a distinct correlation between PPy counter ion and cell viability. Specifically, NSC viability was high on PPy(DBS) but low on PPy containing TsO, ClO4 and Cl. On PPy(DBS), NSC proliferation and differentiation was comparable to standard NSC culture on tissue culture polystyrene. Electrical reduction of PPy(DBS) created a switch for neural stem cell viability, with widespread cell death upon polymer reduction. Coating the PPy(DBS) films with a gel layer composed of a basement membrane matrix efficiently prevented loss of cell viability upon polymer reduction. Here we have defined conditions for the biocompatibility of PPy substrates with NSC culture, critical for the development of devices based on conducting polymers interfacing with NSCs. PMID:21494605

  6. A Rate Feedback Predictive Control Scheme Based on Neural Network and Control Theory for Autonomic Communication

    NASA Astrophysics Data System (ADS)

    Xiong, Naixue; Vasilakos, Athanasios V.; Yang, Laurence T.; Long, Fei; Shu, Lei; Li, Yingshu

    The main difficulty arising in designing an efficient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this chapter describes a novel congestion control scheme that is based on a Back Propagation (BP) neural network technique.We consider a general computer communication model with multiple sources and one destination node. The dynamic buffer occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.

  7. Control of Complex Dynamic Systems by Neural Networks

    NASA Technical Reports Server (NTRS)

    Spall, James C.; Cristion, John A.

    1993-01-01

    This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

  8. Neural network setpoint control of an advanced test reactor experiment loop simulation

    SciTech Connect

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1990-09-01

    This report describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for three neural network designs are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 9 refs., 28 figs., 2 tabs.

  9. Multiobjective algebraic synthesis of neural control systems by implicit model following.

    PubMed

    Ferrari, Silvia

    2009-03-01

    The advantages brought about by using classical linear control theory in conjunction with neural approximators have long been recognized in the literature. In particular, using linear controllers to obtain the starting neural control design has been shown to be a key step for the successful development and implementation of adaptive-critic neural controllers. Despite their adaptive capabilities, neural controllers are often criticized for not providing the same performance and stability guarantees as classical linear designs. Therefore, this paper develops an algebraic synthesis procedure for designing dynamic output-feedback neural controllers that are closed-loop stable and meet the same performance objectives as any classical linear design. The performance synthesis problem is addressed by deriving implicit model-following algebraic relationships between model matrices, obtained from the classical design, and the neural control parameters. Additional linear matrix inequalities (LMIs) conditions for closed-loop exponential stability of the neural controller are derived using existing integral quadratic constraints (IQCs) for operators with repeated slope-restricted nonlinearities. The approach is demonstrated by designing a recurrent neural network controller for a highly maneuverable tailfin-controlled missile that meets multiple design objectives, including pole placement for transient tuning, H(infinity) and H(2) performance in the presence of parameter uncertainty, and command-input tracking. PMID:19203887

  10. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

  11. Neural and hormonal control of postecdysial behaviors in insects.

    PubMed

    White, Benjamin H; Ewer, John

    2014-01-01

    The shedding of the old exoskeleton that occurs in insects at the end of a molt (a process called ecdysis) is typically followed by the expansion and tanning of a new one. At the adult molt, these postecdysial processes include expansion and hardening of the wings. Here we describe recent advances in understanding the neural and hormonal control of wing expansion and hardening, focusing on work using Drosophila melanogaster in which genetic manipulations have permitted detailed investigation of postecdysial processes and their modulation by sensory input. To place this work in context, we briefly review recent progress in understanding the neuroendocrine regulation of ecdysis, which appears to be largely conserved across insect species. Investigations into the neuroendocrine networks that regulate ecdysial and postecdysial behaviors provide insights into how stereotyped, yet environmentally responsive, sequences are generated and how they develop and evolve.

  12. Hypothetical neural control of human bipedal walking with voluntary modulation.

    PubMed

    Jo, Sungho

    2008-02-01

    A hypothetical neuromusculoskeletal model is developed to simulate human normal walking and its modulated behaviors. A small set of neural periodic patterns drive spinal muscle synergies which in turn lead to specific pattern of muscle activation and supraspinal feedback systems maintain postural balance during walking. Then, the model demonstrates modulated behaviors by superimposing voluntary perturbations on the underlying walking pattern. Motions of kicking a ball and obstacle avoidance during walking are simulated as examples. The superposition of the new pulse command to a set of invariant pulses representing spino-locomotor is sufficient to achieve the coordinated behaviors. Also, forward bent walking motion is demonstrated by applying similar superposition. The composition of activations avoids a complicated computation of motor program for a specific task and presents a simple control scheme for different walking patterns.

  13. Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator

    NASA Astrophysics Data System (ADS)

    Rahmani, Mehran; Ghanbari, Ahmad; Ettefagh, Mir Mohammad

    2016-12-01

    This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm robot manipulator. A fraction integral terminal sliding mode control applies to the Inchworm robot manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.

  14. Neural Dynamics of Attentional Cross-Modality Control

    PubMed Central

    Rabinovich, Mikhail; Tristan, Irma; Varona, Pablo

    2013-01-01

    Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing. PMID:23696890

  15. [Neural control of the peripheral circulation and blood pressure].

    PubMed

    Estañol, Bruno; Porras-Betancourt, Manuel; Sánchez-Torres, Gustavo; Martínez-Memije, Raúl; Infante, Oscar; Sentíes-Madrid, Horacio

    2009-12-01

    In the XIX century Claude Bernard discovered the action of the nervous system on the peripheral circulation. In the first half of the XX century Ewald Hering discovered the baro-receptor and the reflex control of the heart rate and blood pressure. Cowley and Guyton demonstrated that sino-aortic denervation induces persistent changes in the blood pressure in the dog. The autonomic nervous system is mainly responsible for the regulation of the circulation and blood pressure in the short term on a beat to beat basis. It controls the vasomotor tone, the heart rate and the cardiac output. With the advent of non invasive methods that measure the blood pressure on a beat to beat basis (Finapres) and with the methods of measurement of the variability of the blood pressure in the frequency domain (spectral analysis) we can currently measure many variables including heart rate, blood pressure, stroke volume, peripheral resistances and the baroreceptor sensitivity and make some inferences about their control mechanisms. These variables can be measured at rest in the supine position, standing up, during rhythmic breathing and during the Valsalva maneuver. In this article we present a review of the neural control of the blood pressure and heart rate.

  16. Neural control of heart rate: the role of neuronal networking.

    PubMed

    Kember, G; Armour, J A; Zamir, M

    2011-05-21

    Neural control of heart rate, particularly its sympathetic component, is generally thought to reside primarily in the central nervous system, though accumulating evidence suggests that intrathoracic extracardiac and intrinsic cardiac ganglia are also involved. We propose an integrated model in which the control of heart rate is achieved via three neuronal "levels" representing three control centers instead of the conventional one. Most importantly, in this model control is effected through networking between neuronal populations within and among these layers. The results obtained indicate that networking serves to process demands for systemic blood flow before transducing them to cardiac motor neurons. This provides the heart with a measure of protection against the possibility of "overdrive" implied by the currently held centrally driven system. The results also show that localized networking instabilities can lead to sporadic low frequency oscillations that have the characteristics of the well-known Mayer waves. The sporadic nature of Mayer waves has been unexplained so far and is of particular interest in clinical diagnosis.

  17. Active Control of Wind-Tunnel Model Aeroelastic Response Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Scott, Robert C.

    2000-01-01

    NASA Langley Research Center, Hampton, VA 23681 Under a joint research and development effort conducted by the National Aeronautics and Space Administration and The Boeing Company (formerly McDonnell Douglas) three neural-network based control systems were developed and tested. The control systems were experimentally evaluated using a transonic wind-tunnel model in the Langley Transonic Dynamics Tunnel. One system used a neural network to schedule flutter suppression control laws, another employed a neural network in a predictive control scheme, and the third employed a neural network in an inverse model control scheme. All three of these control schemes successfully suppressed flutter to or near the limits of the testing apparatus, and represent the first experimental applications of neural networks to flutter suppression. This paper will summarize the findings of this project.

  18. Robustness of a distributed neural network controller for locomotion in a hexapod robot

    NASA Technical Reports Server (NTRS)

    Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

    1992-01-01

    A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

  19. Establishing neural crest identity: a gene regulatory recipe

    PubMed Central

    Simões-Costa, Marcos; Bronner, Marianne E.

    2015-01-01

    The neural crest is a stem/progenitor cell population that contributes to a wide variety of derivatives, including sensory and autonomic ganglia, cartilage and bone of the face and pigment cells of the skin. Unique to vertebrate embryos, it has served as an excellent model system for the study of cell behavior and identity owing to its multipotency, motility and ability to form a broad array of cell types. Neural crest development is thought to be controlled by a suite of transcriptional and epigenetic inputs arranged hierarchically in a gene regulatory network. Here, we examine neural crest development from a gene regulatory perspective and discuss how the underlying genetic circuitry results in the features that define this unique cell population. PMID:25564621

  20. Plasma control using neural network and optical emission spectroscopy

    SciTech Connect

    Kim, Byungwhan; Bae, Jung Ki; Hong, Wan-Shick

    2005-03-01

    Due to high sensitivity to process parameters, plasma processes should be tightly controlled. For plasma control, a predictive model was constructed using a neural network and optical emission spectroscopy (OES). Principal component analysis (PCA) was used to reduce OES dimensionality. This approach was applied to an oxide plasma etching conducted in a CHF{sub 3}/CF{sub 4} magnetically enhanced reactive ion plasma. The etch process was systematically characterized by means of a statistical experimental design. Three etch outputs (etch rate, profile angle, and etch rate nonuniformity) were modeled using three different approaches, including conventional, OES, and PCA-OES models. For all etch outputs, OES models demonstrated improved predictions over the conventional or PCA-OES models. Compared to conventional models, OES models yielded an improvement of more than 25% in modeling profile angle and etch rate nonuniformtiy. More than 40% improvement over PCA-OES model was achieved in modeling etch rate and profile angle. These results demonstrate that nonreduced in situ data are more beneficial than reduced one in constructing plasma control model.

  1. Autonomic neural control of heart rate during dynamic exercise: revisited

    PubMed Central

    White, Daniel W; Raven, Peter B

    2014-01-01

    The accepted model of autonomic control of heart rate (HR) during dynamic exercise indicates that the initial increase is entirely attributable to the withdrawal of parasympathetic nervous system (PSNS) activity and that subsequent increases in HR are entirely attributable to increases in cardiac sympathetic activity. In the present review, we sought to re-evaluate the model of autonomic neural control of HR in humans during progressive increases in dynamic exercise workload. We analysed data from both new and previously published studies involving baroreflex stimulation and pharmacological blockade of the autonomic nervous system. Results indicate that the PSNS remains functionally active throughout exercise and that increases in HR from rest to maximal exercise result from an increasing workload-related transition from a 4 : 1 vagal–sympathetic balance to a 4 : 1 sympatho–vagal balance. Furthermore, the beat-to-beat autonomic reflex control of HR was found to be dependent on the ability of the PSNS to modulate the HR as it was progressively restrained by increasing workload-related sympathetic nerve activity. In conclusion: (i) increases in exercise workload-related HR are not caused by a total withdrawal of the PSNS followed by an increase in sympathetic tone; (ii) reciprocal antagonism is key to the transition from vagal to sympathetic dominance, and (iii) resetting of the arterial baroreflex causes immediate exercise-onset reflexive increases in HR, which are parasympathetically mediated, followed by slower increases in sympathetic tone as workloads are increased. PMID:24756637

  2. Resource allocation in neural networks for motor control

    NASA Astrophysics Data System (ADS)

    Milton, J.; Cummins, J.; Gunnoe, J.; Tollefson, M.; Cabrera, J. L.; Ohira, T.

    2006-03-01

    Multiplicative noise plays an important part of a non-predictive control mechanism for stick balancing at the fingertip. However, intentionally-directed movements are also used in stick balancing, particularly by beginners. The interplay between intentional and non-predictive control mechanisms for stick balancing was assessed using two dual task paradigms: the subject was asked to either move one of their legs rhythmically or to imagine moving their leg while balancing a stick (55.4 cm, 35 g) at their fingertip. Performance was measured by determining the stick survival function, i.e. the fraction of trials (total >=25) for which the stick remained balanced at time t as a function of t. Performance was increased by concurrent rhythmic leg movements (50% survival time shifted from 8-9s to 15s in a typical subject). Imagined movements resulted in a similar improvement (50% survival time of 20s for the above subject) suggesting that this enhancement is not simply related to mechanical vibrations of the fingertip induced by leg movement. These observations emphasize the importance of the development of mathematical models for neural control of skilled motor movements that take into resource allocation of limited resources, such as intention.

  3. Use of artificial neural networks as estimators and controllers

    NASA Astrophysics Data System (ADS)

    Concilio, Antonio; Sorrentino, A.

    1996-04-01

    Active noise control is one among the most promising applications of the so-called Smart Structures, because it ensures, or promises, lower weight, lower cost, more effectiveness and all what is desirable in a vehicle design process, with respect to the current solutions. More and more attention in the research world has been devoting to this argument, pushed by both political, economical and environmental reasons, the one connected to the others. Piezoceramic actuators, integrated into the structure, seem to offer the most fashionable and practical solutions among all the proposed architectures, [1-2]. As sensors, microphones demonstrated to be the most performing, above all because they give the most suitable representation of the field that has to be cancelled, [3-4]. This approach is known as Acousto-Structural Active Control, ASAC, [5]. However, according to Fuller's definition, [6] , an intelligent controller is needed to ensure the development of an "Intelligent Structure" . Its main characteristic should be represented by the capability of learning by examples, of following the structure during its evolution, of being the system "brain" . This peculiarity may be offered by Artificial Neural Networks (ANN's), [7-8]. They present other important features, like the capability, in principle, of treating non-linear as well as linear problems, [9], of identifying dynamic systems, [10], of properly acting as a controller. Then, such a net could integrate in itself the function of "system estimator" or "observer" ,and of interpolator - extrapolator and controller, contemporarily. The authors have been working on such subjects for a long time, proposing for instance ANN's as time-domain structural parameters estimators on a simple 2D element ( a framed plate), [11], as noise and vibration controllers in a FF system, [12-13], as materials damping parameters extractors from experimental data, [14]. All these applications were aimed at noise reduction problems. The

  4. Chemo-mechanical control of neural stem cell differentiation

    NASA Astrophysics Data System (ADS)

    Geishecker, Emily R.

    Cellular processes such as adhesion, proliferation, and differentiation are controlled in part by cell interactions with the microenvironment. Cells can sense and respond to a variety of stimuli, including soluble and insoluble factors (such as proteins and small molecules) and externally applied mechanical stresses. Mechanical properties of the environment, such as substrate stiffness, have also been suggested to play an important role in cell processes. The roles of both biochemical and mechanical signaling in fate modification of stem cells have been explored independently. However, very few studies have been performed to study well-controlled chemo-mechanotransduction. The objective of this work is to design, synthesize, and characterize a chemo-mechanical substrate to encourage neuronal differentiation of C17.2 neural stem cells. In Chapter 2, Polyacrylamide (PA) gels of varying stiffnesses are functionalized with differing amounts of whole collagen to investigate the role of protein concentration in combination with substrate stiffness. As expected, neurons on the softest substrate were more in number and neuronal morphology than those on stiffer substrates. Neurons appeared locally aligned with an expansive network of neurites. Additional experiments would allow for statistical analysis to determine if and how collagen density impacts C17.2 differentiation in combination with substrate stiffness. Due to difficulties associated with whole protein approaches, a similar platform was developed using mixed adhesive peptides, derived from fibronectin and laminin, and is presented in Chapter 3. The matrix elasticity and peptide concentration can be individually modulated to systematically probe the effects of chemo-mechanical signaling on differentiation of C17.2 cells. Polyacrylamide gel stiffness was confirmed using rheological techniques and found to support values published by Yeung et al. [1]. Cellular growth and differentiation were assessed by cell counts

  5. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    PubMed

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme.

  6. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    PubMed

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. PMID:26892402

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

    NASA Astrophysics Data System (ADS)

    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

  8. Temporal dynamics of a homeostatic pathway controlling neural network activity

    PubMed Central

    Bateup, Helen S.; Denefrio, Cassandra L.; Johnson, Caroline A.; Saulnier, Jessica L.; Sabatini, Bernardo L.

    2013-01-01

    Neurons use a variety of mechanisms to homeostatically regulate neural network activity in order to maintain firing in a bounded range. One such process involves the bi-directional modulation of excitatory synaptic drive in response to chronic changes in network activity. Down-scaling of excitatory synapses in response to high activity requires Arc-dependent endocytosis of glutamate receptors. However, the temporal dynamics and signaling pathways regulating Arc during homeostatic plasticity are not well understood. Here we determine the relative contribution of transcriptional and translational control in the regulation of Arc, the signaling pathways responsible for the activity-dependent production of Arc, and the time course of these signaling events as they relate to the homeostatic adjustment of network activity in hippocampal neurons. We find that an ERK1/2-dependent transcriptional pathway active within 1–2 h of up-regulated network activity induces Arc leading to a restoration of network spiking rates within 12 h. Under basal and low activity conditions, specialized mechanisms are in place to rapidly degrade Arc mRNA and protein such that they have half-lives of less than 1 h. In addition, we find that while mTOR signaling is regulated by network activity on a similar time scale, mTOR-dependent translational control is not a major regulator of Arc production or degradation suggesting that the signaling pathways underlying homeostatic plasticity are distinct from those mediating synapse-specific forms of synaptic depression. PMID:24065881

  9. Neural systems in the visual control of steering.

    PubMed

    Field, David T; Wilkie, Richard M; Wann, John P

    2007-07-25

    Visual control of locomotion is essential for most mammals and requires coordination between perceptual processes and action systems. Previous research on the neural systems engaged by self-motion has focused on heading perception, which is only one perceptual subcomponent. For effective steering, it is necessary to perceive an appropriate future path and then bring about the required change to heading. Using function magnetic resonance imaging in humans, we reveal a role for the parietal eye fields (PEFs) in directing spatially selective processes relating to future path information. A parietal area close to PEFs appears to be specialized for processing the future path information itself. Furthermore, a separate parietal area responds to visual position error signals, which occur when steering adjustments are imprecise. A network of three areas, the cerebellum, the supplementary eye fields, and dorsal premotor cortex, was found to be involved in generating appropriate motor responses for steering adjustments. This may reflect the demands of integrating visual inputs with the output response for the control device.

  10. Reinforced ART (ReART) for Online Neural Control

    NASA Astrophysics Data System (ADS)

    Ediriweera, Damjee D.; Marshall, Ian W.

    Fuzzy ART has been proposed for learning stable recognition categories for an arbitrary sequence of analogue input patterns. It uses a match based learning mechanism to categorise inputs based on similarities in their features. However, this approach does not work well for neural control, where inputs have to be categorised based on the classes which they represent, rather than by the features of the input. To address this we propose and investigate ReART, a novel extension to Fuzzy ART. ReART uses a feedback based categorisation mechanism supporting class based input categorisation, online learning, and immunity from the plasticity stability dilemma. ReART is used for online control by integrating it with a separate external function which maps each ReART category to a desired output action. We test the proposal in the context of a simulated wireless data reader intended to be carried by an autonomous mobile vehicle, and show that ReART training time and accuracy are significantly better than both Fuzzy ART and Back Propagation. ReART is also compared to a Naïve Bayesian Classifier. Naïve Bayesian Classification achieves faster learning, but is less accurate in testing compared to both ReART, and Bach Propagation.

  11. Volitional control of neural activity: implications for brain–computer interfaces

    PubMed Central

    Fetz, Eberhard E

    2007-01-01

    Successful operation of brain–computer interfaces (BCI) and brain–machine interfaces (BMI) depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviours, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized. PMID:17234689

  12. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

    PubMed

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

  13. Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems.

    PubMed

    Wang, Min; Chen, Bing; Shi, Peng

    2008-06-01

    This paper proposes a novel adaptive neural control scheme for a class of perturbed strict-feedback nonlinear time-delay systems with unknown virtual control coefficients. Based on the radial basis function neural network online approximation capability, an adaptive neural controller is presented by combining the backstepping approach and Lyapunov-Krasovskii functionals. The proposed controller guarantees the semiglobal boundedness of all the signals in the closed-loop system and contains minimal learning parameters. Finally, three simulation examples are given to demonstrate the effectiveness and applicability of the proposed scheme.

  14. Discrete-time neural inverse optimal control for nonlinear systems via passivation.

    PubMed

    Ornelas-Tellez, Fernando; Sanchez, Edgar N; Loukianov, Alexander G

    2012-08-01

    This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot. PMID:24807528

  15. Global tracking control of strict-feedback systems using neural networks.

    PubMed

    Huang, Jeng-Tze

    2012-11-01

    Most existing adaptive neural controllers ensure semiglobally uniform ultimately bounded stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. As a result, deterioration of tracking performance or even instability may occur in real applications. A common recourse is to activate an extra robust controller outside the neural active region to pull back the transient. Such an approach, however, has been restricted to dynamic systems with matched uncertainty. We extend it to strict-feedback systems with mismatched uncertainties via multiswitching-based backstepping methodology. Each virtual and actual controller of the proposed design switches between an adaptive neural controller and a robust controller, with the switching algorithm being sufficiently smooth and, hence, able to be incorporated with the backstepping tool. The overall controller ensures globally uniform ultimate boundedness while simultaneously avoiding the possible control singularity. Simulation results demonstrate the validity of the proposed designs.

  16. Workshop on neural networks

    SciTech Connect

    Uhrig, R.E.; Emrich, M.L.

    1990-01-01

    The topics covered in this report are: Learning, Memory, and Artificial Neural Systems; Emerging Neural Network Technology; Neural Networks; Digital Signal Processing and Neural Networks; Application of Neural Networks to In-Core Fuel Management; Neural Networks in Process Control; Neural Network Applications in Image Processing; Neural Networks for Multi-Sensor Information Fusion; Neural Network Research in Instruments Controls Division; Neural Networks Research in the ORNL Engineering Physics and Mathematics Division; Neural Network Applications for Linear Programming; Neural Network Applications to Signal Processing and Diagnostics; Neural Networks in Filtering and Control; Neural Network Research at Tennessee Technological University; and Global Minima within the Hopfield Hypercube.

  17. Neural network based approach for tuning of SNS feedback and feedforward controllers.

    SciTech Connect

    Kwon, S. I.; Prokop, M. S.; Regan, A. H.

    2002-01-01

    The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

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

    PubMed

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

    2015-08-01

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

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

    PubMed

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

    2015-08-01

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

  20. Amniotic fluid as a source of multipotent cells for clinical use.

    PubMed

    Young, Bruce K; Chan, Michael K; Liu, Li; Basch, Ross S

    2016-04-01

    Amniotic fluid cells (AFC) from 2nd trimester amniocentesis have been found to be a source of multipotent stem cells which might overcome the limitations of expansion, histocompatibility, tumorigenesis, and ethical issues associated with using human embryonic cells, umbilical cord, cord blood, bone marrow, and induced pluripotent cells. Previous work by our group and others demonstrated multipotency and the ability to grow well in culture. However, all these studies were done in media containing fetal calf serum. We sought to observe the properties of AFC grown in serum-free media as that would be required for clinical transplantation in humans. Fresh samples were obtained from three patients, and each sample divided into a culture whose cells were not exposed to fetal calf serum, and the other half into a standard culture medium containing fetal calf serum. Doubling time and stem cell marker expression by flow cytometry were assessed. Differentiation to neural, osteoid, and chondrogenic lineages was induced using appropriate media and confirmed by fluorescent microscopy, histology, and immunohistochemistry. There were no statistically significant differences between cells grown serum-free and in standard media in any of these parameters. The data supports the possibility of clinical use of AFC in stem cell transplantation.

  1. Nasal septum-derived multipotent progenitors: a potent source for stem cell-based regenerative medicine.

    PubMed

    Shafiee, Abbas; Kabiri, Mahboubeh; Ahmadbeigi, Naser; Yazdani, Saeed Oraee; Mojtahed, Mohammad; Amanpour, Saeid; Soleimani, Masoud

    2011-12-01

    Thus far, autologous adult stem cells have attracted great attention for clinical purposes. In this study, we aimed at identifying and comprehensively characterizing a subpopulation of multipotent cells within human nasal septal cartilage. We also conducted a comparative investigation with other well-established stem cells such as bone marrow-mesenchymal stem cells, adipose tissue-mesenchymal stem cells, and unrestricted somatic stem cells. The isolated clonal population was characterized using immunofluorescence, flow cytometry, reverse transcriptase, and real-time polymerase chain reaction. Nasal septal progenitors (NSP) expressed critical pluripotency and mesoectodermal stem cell markers. They also shared many characteristics with MSC in expression of CD90, CD105, CD106, CD166, and HLA-ABC and lack of expression of CD34, CD45, and HLA-DR. NSP distinctly presented CD133 (Prominin-1). These cells could proliferate rapidly in vitro with a higher clonogenic potential and showed a longer lifespan than other studied cells. This population bears some other multipotent properties in showing a high capacity to be differentiated into other lineages including chondrocytes, osteocytes, and neural-like cell types. Another strong/positive feature of this population was their ability to be safely expanded ex vivo with no susceptibility to chromosomal abnormality or tumorigenicity both in vitro and in vivo. In conclusion, NSP could be considered as an alternative autologous cell source that can bring them to the top of therapeutic applications. PMID:21401444

  2. Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes.

    PubMed

    Fernandez de Canete, J; Gonzalez-Perez, S; Ramos-Diaz, J C

    2012-04-01

    The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose-insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.

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

    SciTech Connect

    Gu, Z.; Rizy, D.T.

    1996-02-01

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

  4. Neural coding in a single sensory neuron controlling opposite seeking behaviours in Caenorhabditis elegans

    PubMed Central

    Kuhara, Atsushi; Ohnishi, Noriyuki; Shimowada, Tomoyasu; Mori, Ikue

    2011-01-01

    Unveiling the neural codes for intricate behaviours is a major challenge in neuroscience. The neural circuit for the temperature-seeking behaviour of Caenorhabditis elegans is an ideal system to dissect how neurons encode sensory information for the execution of behavioural output. Here we show that the temperature-sensing neuron AFD transmits both stimulatory and inhibitory neural signals to a single interneuron AIY. In this circuit, a calcium concentration threshold in AFD acts as a switch for opposing neural signals that direct the opposite behaviours. Remote control of AFD activity, using a light-driven ion pump and channel, reveals that diverse reduction levels of AFD activity can generate warm- or cold-seeking behaviour. Calcium imaging shows that AFD uses either stimulatory or inhibitory neuronal signalling onto AIY, depending on the calcium concentration threshold in AFD. Thus, dual neural regulation in opposite directions is directly coupled to behavioural inversion in the simple neural circuit. PMID:21673676

  5. Decoding Neural Circuits that Control Compulsive Sucrose-Seeking

    PubMed Central

    Nieh, Edward H.; Matthews, Gillian A.; Allsop, Stephen A.; Presbrey, Kara N.; Leppla, Christopher A.; Wichmann, Romy; Neve, Rachael; Wildes, Craig P.; Tye, Kay M.

    2015-01-01

    The lateral hypothalamic (LH) projection to the ventral tegmental area (VTA) has been linked to reward processing, but the computations within the LH-VTA loop that give rise to specific aspects of behavior have been difficult to isolate. We show that LH-VTA neurons encode the learned action of seeking a reward, independent of reward availability. In contrast, LH neurons downstream of VTA encode reward-predictive cues and unexpected reward omission. We show that inhibiting the LH-VTA pathway reduces “compulsive” sucrose-seeking, but not food consumption in hungry mice. We reveal that the LH sends excitatory and inhibitory input onto VTA dopamine (DA) and GABA neurons, and that the GABAergic projection drives feeding-related behavior. Our study overlays information about the type, function and connectivity of LH neurons and identifies a neural circuit that selectively controls compulsive sugar consumption, without preventing feeding necessary for survival, providing a potential target for therapeutic interventions for compulsive-overeating disorder. PMID:25635460

  6. Neural Control of Breathing and CO2 Homeostasis.

    PubMed

    Guyenet, Patrice G; Bayliss, Douglas A

    2015-09-01

    Recent advances have clarified how the brain detects CO2 to regulate breathing (central respiratory chemoreception). These mechanisms are reviewed and their significance is presented in the general context of CO2/pH homeostasis through breathing. At rest, respiratory chemoreflexes initiated at peripheral and central sites mediate rapid stabilization of arterial PCO2 and pH. Specific brainstem neurons (e.g., retrotrapezoid nucleus, RTN; serotonergic) are activated by PCO2 and stimulate breathing. RTN neurons detect CO2 via intrinsic proton receptors (TASK-2, GPR4), synaptic input from peripheral chemoreceptors and signals from astrocytes. Respiratory chemoreflexes are arousal state dependent whereas chemoreceptor stimulation produces arousal. When abnormal, these interactions lead to sleep-disordered breathing. During exercise, central command and reflexes from exercising muscles produce the breathing stimulation required to maintain arterial PCO2 and pH despite elevated metabolic activity. The neural circuits underlying central command and muscle afferent control of breathing remain elusive and represent a fertile area for future investigation. PMID:26335642

  7. Gastrointestinal Parasites and the Neural Control of Gut Functions

    PubMed Central

    Halliez, Marie C. M.; Buret, André G.

    2015-01-01

    Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS), which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the ENS. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. Giardia intestinalis, Giardia lamblia), Trypanosoma cruzi, Schistosoma species and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as C. parvum and G. duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities. PMID:26635531

  8. Neural mechanisms of timing control in a coincident timing task.

    PubMed

    Masaki, Hiroaki; Sommer, Werner; Takasawa, Noriyoshi; Yamazaki, Katuo

    2012-04-01

    Many ball sports such as tennis or baseball require precise temporal anticipation of both sensory input and motor output (i.e., receptor anticipation and effector anticipation, respectively) and close performance monitoring. We investigated the neural mechanisms underlying timing control and performance monitoring in a coincident timing task involving both types of anticipations. Peak force for two time-to-peak force (TTP) conditions-recorded with a force-sensitive key-was required to coincide with a specific position of a stimulus rotating either slow or fast on a clock face while the contingent negative variation (CNV) and the motor-elicited negativity were recorded. Absolute timing error was generally smaller for short TTP (high velocity) conditions. CNV amplitudes increased with both faster stimulus velocity and longer TTPs possibly reflecting increased motor programming efforts. In addition, the motor-elicited negativity was largest in the slow stimulus/short TTP condition, probably representing some forms of performance monitoring as well as shorter response duration. Our findings indicate that the coincident timing task is a good model for real-life situations of tool use. PMID:22415201

  9. Neural Control of Breathing and CO2 Homeostasis.

    PubMed

    Guyenet, Patrice G; Bayliss, Douglas A

    2015-09-01

    Recent advances have clarified how the brain detects CO2 to regulate breathing (central respiratory chemoreception). These mechanisms are reviewed and their significance is presented in the general context of CO2/pH homeostasis through breathing. At rest, respiratory chemoreflexes initiated at peripheral and central sites mediate rapid stabilization of arterial PCO2 and pH. Specific brainstem neurons (e.g., retrotrapezoid nucleus, RTN; serotonergic) are activated by PCO2 and stimulate breathing. RTN neurons detect CO2 via intrinsic proton receptors (TASK-2, GPR4), synaptic input from peripheral chemoreceptors and signals from astrocytes. Respiratory chemoreflexes are arousal state dependent whereas chemoreceptor stimulation produces arousal. When abnormal, these interactions lead to sleep-disordered breathing. During exercise, central command and reflexes from exercising muscles produce the breathing stimulation required to maintain arterial PCO2 and pH despite elevated metabolic activity. The neural circuits underlying central command and muscle afferent control of breathing remain elusive and represent a fertile area for future investigation.

  10. Neural mechanisms of timing control in a coincident timing task.

    PubMed

    Masaki, Hiroaki; Sommer, Werner; Takasawa, Noriyoshi; Yamazaki, Katuo

    2012-04-01

    Many ball sports such as tennis or baseball require precise temporal anticipation of both sensory input and motor output (i.e., receptor anticipation and effector anticipation, respectively) and close performance monitoring. We investigated the neural mechanisms underlying timing control and performance monitoring in a coincident timing task involving both types of anticipations. Peak force for two time-to-peak force (TTP) conditions-recorded with a force-sensitive key-was required to coincide with a specific position of a stimulus rotating either slow or fast on a clock face while the contingent negative variation (CNV) and the motor-elicited negativity were recorded. Absolute timing error was generally smaller for short TTP (high velocity) conditions. CNV amplitudes increased with both faster stimulus velocity and longer TTPs possibly reflecting increased motor programming efforts. In addition, the motor-elicited negativity was largest in the slow stimulus/short TTP condition, probably representing some forms of performance monitoring as well as shorter response duration. Our findings indicate that the coincident timing task is a good model for real-life situations of tool use.

  11. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    ERIC Educational Resources Information Center

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine M.

    2008-01-01

    Purpose: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural…

  12. Neural networks as a possible architecture for the distributed control of space systems

    NASA Technical Reports Server (NTRS)

    Fiesler, E.; Choudry, A.

    1987-01-01

    Researchers attempted to identify the features essential for large, complex, multi-modular multi-functional systems possessing a high level of interconnectivity. These features were studied in the context of neural networks with the aim of arriving at a possible architecture of the distributed control system-specific features of the neural networks and their applicability in space systems.

  13. Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2005-01-01

    This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

  14. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

    PubMed Central

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  15. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    PubMed

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

  16. A study of interceptor attitude control based on adaptive wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Li, Da; Wang, Qing-chao

    2005-12-01

    This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.

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

    NASA Astrophysics Data System (ADS)

    Kuljaca, Ognjen

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

  18. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method

    PubMed Central

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method. PMID:27293422

  19. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

    PubMed

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method. PMID:27293422

  20. Experiments in Neural-Network Control of a Free-Flying Space Robot

    NASA Technical Reports Server (NTRS)

    Wilson, Edward

    1995-01-01

    Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

  1. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  2. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    SciTech Connect

    Toomarian, N.; Kirkham, H.

    1993-12-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  3. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    NASA Astrophysics Data System (ADS)

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-01

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  4. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    SciTech Connect

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-12

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  5. Neural crest specification by inhibition of the ROCK/Myosin II pathway

    PubMed Central

    Kim, Kyeongmi; Ossipova, Olga; Sokol, Sergei Y.

    2015-01-01

    Neural crest is a population of multipotent progenitor cells that form at the border of neural and non-neural ectoderm in vertebrate embryos, and undergo epithelialmesenchymal transition and migration. According to the traditional view, the neural crest is specified in early embryos by signaling molecules including BMP, FGF and Wnt proteins. Here we identify a novel signaling pathway leading to neural crest specification, which involves Rho-associated kinase (ROCK) and its downstream target non-muscle Myosin II. We show that ROCK inhibitors promote differentiation of human embryonic stem cells into neural crest-like progenitors (NCPs) that are characterized by specific molecular markers and ability to differentiate into multiple cell types, including neurons, chondrocytes, osteocytes and smooth muscle cells. Moreover, inhibition of Myosin II was sufficient for generating NCPs at high efficiency. Whereas Myosin II has been previously implicated in the self-renewal and survival of human pluripotent ES cells, we demonstrate its role in neural crest development during ES cell differentiation. Inhibition of this pathway in Xenopus embryos expanded neural crest in vivo, further indicating that neural crest specification is controlled by ROCK-dependent Myosin II activity. We propose that changes in cell morphology in response to ROCK and Myosin II inhibition initiate mechanical signaling leading to neural crest fates. PMID:25346532

  6. Neural network control for position tracking of a two-axis inverted pendulum system: experimental studies.

    PubMed

    Jung, Seul; Cho, Hyun-Taek; Hsia, T C

    2007-07-01

    In this paper, experimental studies of a decentralized neural network control scheme of the reference compensation technique applied to control a 2-degrees-of-freedom (2-DOF) inverted pendulum on an x - y plane are presented. Each axis is controlled by two separate neural network controllers to have a decoupled control structure. Neural network controllers are applied not only to balance the angle of pendulum, but also to control the position tracking of the cart. The decoupled control structure can compensate for uncertainties and cancel coupling effects. Especially, a circular trajectory tracking task is tested for position tracking control of the cart while maintaining the angle of the pendulum. Experimental result shows that position control of the inverted pendulum and cart is successful.

  7. The Relationship of Aluminium and Silver to Neural Tube Defects; a Case Control

    PubMed Central

    Ramírez-Altamirano, María de Jesús; Fenton-Navarro, Patricia; Sivet-Chiñas, Elvira; Harp-Iturribarria, Flor de María; Martínez-Cruz, Ruth; Cruz, Pedro Hernández; Cruz, Margarito Martínez; Pérez-Campos, Eduardo

    2012-01-01

    Objective The purpose of this study was to identify the relationship of neurotoxic inorganic elements in the hair of patients with the diagnosis of Neural Tube Defects. Our initial hypothesis was that neurotoxic inorganic elements were associated with Neural Tube Defects. Methods Twenty-three samples of hair from newborns were obtained from the General Hospital, “Aurelio Valdivieso” in the city of Oaxaca, Mexico. The study group included 8 newborn infants with neural tube pathology. The control group was composed of 15 newborns without this pathology. The presence of inorganic elements in the hair samples was determined by inductively-coupled plasma spectroscopy (spectroscopic emission of the plasma). Findings The population of newborns with Neural Tube Defects showed significantly higher values of the following elements than the control group: Aluminium, Neural Tube Defects 152.77±51.06 µg/g, control group 76.24±27.89 µg/g; Silver, Neural Tube Defects 1.45±0.76, control group 0.25±0.53 µg/g; Potassium, Neural Tube Defects 553.87±77.91 µg/g, control group 341.13±205.90 µg/g. Association was found at 75 percentile between aluminium plus silver, aluminium plus potassium, silver plus potassium, and potassium plus sodium. Conclusion In the hair of newborns with Neural Tube Defects, the following metals were increased: aluminium, silver. Given the neurotoxicity of the same, and association of Neural Tube Defects with aluminum and silver, one may infer that they may be participating as factors in the development of Neural Tube Defects. PMID:23400307

  8. Cognitive Control Mechanisms, Emotion & Memory: A neural perspective with implications for psychopathology

    PubMed Central

    Banich, Marie T.; Mackiewicz, Kristen L.; Depue, Brendan E.; Whitmer, Anson; Miller, Gregory A.; Heller, Wendy

    2009-01-01

    In this paper we provide a focused review of the literature examining neural mechanisms involved in cognitive control over memory processes that can influence, and in turn are influenced, by emotional processes. The review is divided into two parts, the first focusing on working memory and the second on long-term memory. With regard to working memory, we discuss the neural bases of 1) control mechanisms that can select against distracting emotional information, 2) mechanisms that can regulate emotional reactions or responses, 3) how mood state influences cognitive control, and 4) individual differences in control mechanisms. For long-term memory, we briefly review 1) the neural substrates of emotional memory, 2) the cognitive and neural mechanisms that are involved in controlling emotional memories and 3) how these systems are altered in post-traumatic stress disorder. Finally, we consider tentative generalizations that can be drawn from this relatively unexplored conjunction of research endeavors. PMID:18948135

  9. Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients.

    PubMed

    Ge, Shuzhi Sam; Hong, Fan; Lee, Tong Heng

    2004-02-01

    In this paper, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. The proposed design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. The unknown time delays are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. It is proved that the proposed backstepping design method is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop. In addition, the output of the system is proven to converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

  10. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    NASA Astrophysics Data System (ADS)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

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

    PubMed

    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.

  12. The neural representation of postural control in humans

    PubMed Central

    Karnath, Hans-Otto; Ferber, Susanne; Dichgans, Johannes

    2000-01-01

    Lesion of the “vestibular cortex” in the human posterior insula leads to a tilted perception of visual vertical but not to tilted body posture and loss of lateral balance. However, some stroke patients show the reverse pattern. Although their processing of visual and vestibular inputs for orientation perception of the visual world is undisturbed, they push away actively from the ipsilesional side (the side of lesion location), leading to a contraversive tilt of the body (tilt toward the side opposite to the lesion) and falling to that side. Recently, the origin of contraversive pushing was identified as an altered perception of the body's orientation in relation to gravity. These patients experience their body as oriented “upright” when actually tilted enormously to the ipsilesional side (18° on average). The findings argued for a separate pathway in humans for sensing body orientation in relation to gravity apart from the one projecting to the vestibular cortex. The present study aimed at identifying this brain area. The infarcted brain regions of 23 consecutively admitted patients with severe contraversive pushing were projected onto a template MRI scan, which had been normalized to Talairach space. The overlapping area of these infarctions centered on the posterolateral thalamus. Our finding necessitates reinterpretation of this area as being only a “relay structure” of the vestibular pathway on its way from the brainstem to the vestibular cortex. The ventral posterior and lateral posterior nuclei of the posterolateral thalamus (and probably its cortical projections) rather seem to be fundamentally involved in the neural representation of a second graviceptive system in humans decisive for our control of upright body posture. PMID:11087818

  13. Multipotent neurogenic fate of mesenchymal stem cell is determined by Cdk4-mediated hypophosphorylation of Smad-STAT3.

    PubMed

    Kim, Dong-Young; Lee, Janet; Kang, Dongrim; Lee, Do-Hyeong; Kim, Yoon-Ja; Hwang, Sang-Gu; Kim, Dong-Ik; Lee, Chang-Woo; Lee, Kyung-Hoon

    2016-07-01

    Cyclin-dependent kinase (Cdk) in complex with a corresponding cyclin plays a pivotal role in neurogenic differentiation. In particular, Cdk4 activity acts as a signaling switch to direct human mesenchymal stem cells (MSCs) to neural transdifferentiation. However, the molecular evidence of how Cdk4 activity converts MSCs to neurogenic lineage remains unknown. Here, we found that Cdk4 inhibition in human MSCs enriches the populations of neural stem and progenitor pools rather than differentiated glial and neuronal cell pools. Interestingly, Cdk4 inhibition directly inactivates Smads and subsequently STAT3 signaling by hypophosphorylation, and both Cdk4 and Smads levels are linked during the processes of neural transdifferentiation and differentiation. In summary, our results provide novel molecular evidence in which Cdk4 inhibition leads to directing human MSCs to a multipotent neurogenic fate by inactivating Smads-STAT3 signaling. PMID:27192561

  14. A tensor-product-kernel framework for multiscale neural activity decoding and control.

    PubMed

    Li, Lin; Brockmeier, Austin J; Choi, John S; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2014-01-01

    Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

  15. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    PubMed

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method.

  16. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    SciTech Connect

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  17. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    PubMed

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. PMID:26506019

  18. A Wirelessly Powered and Controlled Device for Optical Neural Control of Freely-Behaving Animals

    PubMed Central

    Wentz, Christian T.; Bernstein, Jacob G.; Monahan, Patrick; Guerra, Alexander; Rodriguez, Alex; Boyden, Edward S.

    2011-01-01

    Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists’ capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted LED, tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the high-frequency pulse trains often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 grams capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2W of power to the LEDs in steady state, and 4.3W in bursts. We also present an optional radio transceiver module (1 gram) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be simultaneously controlled from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control. PMID:21701058

  19. A wirelessly powered and controlled device for optical neural control of freely-behaving animals

    NASA Astrophysics Data System (ADS)

    Wentz, Christian T.; Bernstein, Jacob G.; Monahan, Patrick; Guerra, Alexander; Rodriguez, Alex; Boyden, Edward S.

    2011-08-01

    Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements contribute to normal and pathological brain functions. Typically, awake behaving experiments are conducted by inserting an optical fiber into the brain, tethered to a remote laser, or by utilizing an implanted light-emitting diode (LED), tethered to a remote power source. A fully wireless system would enable chronic or longitudinal experiments where long duration tethering is impractical, and would also support high-throughput experimentation. However, the high power requirements of light sources (LEDs, lasers), especially in the context of the extended illumination periods often desired in experiments, precludes battery-powered approaches from being widely applicable. We have developed a headborne device weighing 2 g capable of wirelessly receiving power using a resonant RF power link and storing the energy in an adaptive supercapacitor circuit, which can algorithmically control one or more headborne LEDs via a microcontroller. The device can deliver approximately 2 W of power to the LEDs in steady state, and 4.3 W in bursts. We also present an optional radio transceiver module (1 g) which, when added to the base headborne device, enables real-time updating of light delivery protocols; dozens of devices can be controlled simultaneously from one computer. We demonstrate use of the technology to wirelessly drive cortical control of movement in mice. These devices may serve as prototypes for clinical ultra-precise neural prosthetics that use light as the modality of biological control.

  20. Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control.

    PubMed

    Zhang, Guodong; Shen, Yi

    2014-07-01

    This paper investigates the exponential synchronization of coupled memristor-based chaotic neural networks with both time-varying delays and general activation functions. And here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new criteria ensuring exponential synchronization of two memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the new proposed results here are very easy to verify and also complement, extend the earlier publications. Numerical simulations on the chaotic systems are presented to illustrate the effectiveness of the theoretical results.

  1. Finite-time synchronization control of a class of memristor-based recurrent neural networks.

    PubMed

    Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun

    2015-03-01

    This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method.

  2. Synchronization control of memristor-based recurrent neural networks with perturbations.

    PubMed

    Wang, Weiping; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian

    2014-05-01

    In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.

  3. Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  4. Method for neural network control of motion using real-time environmental feedback

    NASA Technical Reports Server (NTRS)

    Buckley, Theresa M. (Inventor)

    1997-01-01

    A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

  5. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  6. Model-Following Controller Based on Neural Network for Variable Displacement Pump

    NASA Astrophysics Data System (ADS)

    Chu, Ming-Hui; Kang, Yuan; Chang, Yih-Fong; Liu, Yuan-Liang; Chang, Chuan-Wei

    The variable displacement axial piston pump (VDAPP) is inherently nonlinear, time variant and subjected to load disturbance. The controls of flow and pressure of VDAPP are achieved by changing the swashplate angle. The swashplate actuators are controlled by an electro-hydraulic proportional valve (EHPV). It is reasonable for swashplate angle of a VDAPP to employ neural network based on adaptive control. In this study, the nonlinear model of the VDAPP with a three-way electro-hydraulic proportional valve is proposed, and a neural network model-following controller is designed to control the swashplate swivel angle. The time response for the swashplate angle is analyzed by simulation and experiment, and a favorable model-following characteristic is achieved. The proposed neural controller can conduct nonlinear control in VDAPP, enhance adaptability and robustness, and improve the performance of the control system.

  7. A Design of Fuzzy Neural Network Based Robust Gain Scheduling Controllers

    NASA Astrophysics Data System (ADS)

    Sato, Yoshishige

    This paper propose robust gain scheduling control design by intelligent control which uses Fuzzy-Neural Network without model. Proposal methods are as follows, To constitute a robust and capable of automatically gain controlling against the conventional fixed PID control system. To build the Neural Network which learns inverse dynamics as feed forward compensation, and to build 2 degrees freedom control which is the feedback compensation. To propose the control system which adaptively adjusts the gain according to the changes of target errors, and to verified the effectiveness of the proposed method.

  8. Control of Wind Tunnel Operations Using Neural Net Interpretation of Flow Visualization Records

    NASA Technical Reports Server (NTRS)

    Buggele, Alvin E.; Decker, Arthur J.

    1994-01-01

    Neural net control of operations in a small subsonic/transonic/supersonic wind tunnel at Lewis Research Center is discussed. The tunnel and the layout for neural net control or control by other parallel processing techniques are described. The tunnel is an affordable, multiuser platform for testing instrumentation and components, as well as parallel processing and control strategies. Neural nets have already been tested on archival schlieren and holographic visualizations from this tunnel as well as recent supersonic and transonic shadowgraph. This paper discusses the performance of neural nets for interpreting shadowgraph images in connection with a recent exercise for tuning the tunnel in a subsonic/transonic cascade mode of operation. That mode was operated for performing wake surveys in connection with NASA's Advanced Subsonic Technology (AST) noise reduction program. The shadowgraph was presented to the neural nets as 60 by 60 pixel arrays. The outputs were tunnel parameters such as valve settings or tunnel state identifiers for selected tunnel operating points, conditions, or states. The neural nets were very sensitive, perhaps too sensitive, to shadowgraph pattern detail. However, the nets exhibited good immunity to variations in brightness, to noise, and to changes in contrast. The nets are fast enough so that ten or more can be combined per control operation to interpret flow visualization data, point sensor data, and model calculations. The pattern sensitivity of the nets will be utilized and tested to control wind tunnel operations at Mach 2.0 based on shock wave patterns.

  9. Neural Control of the Cardiovascular System in Space

    NASA Technical Reports Server (NTRS)

    Levine, Benjamin D.; Pawelczyk, James A.; Zuckerman, Julie; Zhang, Rong; Fu, Qi; Iwasaki, Kenichi; Ray, Chet; Blomqvist, C. Gunnar; Lane, Lynda D.; Giller, Cole A.

    2003-01-01

    During the acute transition from lying supine to standing upright, a large volume of blood suddenly moves from the chest into the legs. To prevent fainting, the blood pressure control system senses this change immediately, and rapidly adjusts flow (by increasing heart rate) and resistance to flow (by constricting the blood vessels) to restore blood pressure and maintain brain blood flow. If this system is inadequate, the brain has a backup plan. Blood vessels in the brain can adjust their diameter to keep blood flow constant. If blood pressure drops, the brain blood vessels dilate; if blood pressure increases, the brain blood vessels constrict. This process, which is called autoregulation, allows the brain to maintain a steady stream of oxygen, even when blood pressure changes. We examined what changes in the blood pressure control system or cerebral autoregulation contribute to the blood pressure control problems seen after spaceflight. We asked: (1) does the adaptation to spaceflight cause an adaptation in the blood pressure control system that impairs the ability of the system to constrict blood vessels on return to Earth?; (2) if such a defect exists, could we pinpoint the neural pathways involved?; and (3) does cerebral autoregulation become abnormal during spaceflight, impairing the body s ability to maintain constant brain blood flow when standing upright on Earth? We stressed the blood pressure control system using lower body negative pressure, upright tilt, handgrip exercise, and cold stimulation of the hand. Standard cardiovascular parameters were measured along with sympathetic nerve activity (the nerve activity causing blood vessels to constrict) and brain blood flow. We confirmed that the primary cardiovascular effect of spaceflight was a postflight reduction in upright stroke volume (the amount of blood the heart pumps per beat). Heart rate increased appropriately for the reduction in stroke volume, thereby showing that changes in heart rate

  10. Neural Network for Image-to-Image Control of Optical Tweezers

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

    2004-01-01

    A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

  11. Manipulator adaptive control by neural networks in an orange picking robot.

    PubMed

    Cavalieri, S; Plebe, A

    1996-12-01

    The paper focuses on the use of neural networks for process identification in an orange-picking robot adaptive control system. The results that will be shown in the paper refer to a study carried out under the European Community ESPRIT project "CONNY", dealing with the application of neural networks to robotics. The aim of the research is to verify the possibility of integrating a neural identification module in a traditional system to control the movement of the manipulators of the robot. The paper illustrates integration of neural identification in the existing orange-picking robot control system, highlighting the improvement of performance obtainable. Although the proposal refers to a specific robot, it can be applied to any system with the same dynamic features. PMID:9113534

  12. Neural networks for control of NO{sub x} emissions in fossil plants

    SciTech Connect

    Reifman, J.; Feldman, E.E.

    1997-04-01

    We discuss the use of two classes of artificial neural networks, multilayer feedforward networks and fully-recurrent networks, in the development of a closed-loop controller for discrete-time dynamical systems. We apply the neural system to the control of oxides of nitrogen (NO{sub x}) emissions for a simplified representation of a furnace of a coal-fired fossil plant. Plant data from one of Commonwealth Edison`s fossil power plants were used to build a recurrent neural model of NO{sub x} formation which is then used in the training of the feedforward neural controller. Preliminary simulation results demonstrate the feasibility of the approach and additional tests with increasingly realistic models should be pursued.

  13. Multivariate biophysical markers predictive of mesenchymal stromal cell multipotency

    PubMed Central

    Lee, Wong Cheng; Shi, Hui; Poon, Zhiyong; Nyan, Lin Myint; Kaushik, Tanwi; Shivashankar, G. V.; Chan, Jerry K. Y.; Lim, Chwee Teck; Han, Jongyoon; Van Vliet, Krystyn J.

    2014-01-01

    The capacity to produce therapeutically relevant quantities of multipotent mesenchymal stromal cells (MSCs) via in vitro culture is a common prerequisite for stem cell-based therapies. Although culture expanded MSCs are widely studied and considered for therapeutic applications, it has remained challenging to identify a unique set of characteristics that enables robust identification and isolation of the multipotent stem cells. New means to describe and separate this rare cell type and its downstream progenitor cells within heterogeneous cell populations will contribute significantly to basic biological understanding and can potentially improve efficacy of stem and progenitor cell-based therapies. Here, we use multivariate biophysical analysis of culture-expanded, bone marrow-derived MSCs, correlating these quantitative measures with biomolecular markers and in vitro and in vivo functionality. We find that, although no single biophysical property robustly predicts stem cell multipotency, there exists a unique and minimal set of three biophysical markers that together are predictive of multipotent subpopulations, in vitro and in vivo. Subpopulations of culture-expanded stromal cells from both adult and fetal bone marrow that exhibit sufficiently small cell diameter, low cell stiffness, and high nuclear membrane fluctuations are highly clonogenic and also exhibit gene, protein, and functional signatures of multipotency. Further, we show that high-throughput inertial microfluidics enables efficient sorting of committed osteoprogenitor cells, as distinct from these mesenchymal stem cells, in adult bone marrow. Together, these results demonstrate novel methods and markers of stemness that facilitate physical isolation, study, and therapeutic use of culture-expanded, stromal cell subpopulations. PMID:25298531

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

    NASA Technical Reports Server (NTRS)

    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.

  15. Prediction by neural network methods compared for energy control problems

    SciTech Connect

    Surkan, A.J.; Skurikhin, A.N.

    1996-10-01

    Daily recordings of energy consumption are sequenced to construct a time series that is used to test the accuracy of a variety of neural networks in making one- and two-day demand predictions. Five neural network methods were applied to the same data for a problem of predicting daily energy usage. These included four which were gradient-based, namely, back propagation (BACKPROP), quick-propagation (QUICKPROP), cascade correlation (CASCOR), memory neuron network (MNN), and a correlation-based method called ALOPEX. The observed time series is the only information source used for making predictions. Other supplementary parallel series of independent data can produce improvements. It was demonstrated that these neural network learning algorithms can train networks to predict consistently, one-day-ahead, over one year of set-aside test patterns and reduce the average error to below 19%. Reported is a comparison of applicability of neural networks by programmed simulations of the widely used gradient-based algorithms along with newer algorithms MNN and ALOPEX. The diverse capabilities of these various networks give insights which are a useful basis for selecting further studies.

  16. Neural Issues in the Control of Muscular Strength

    ERIC Educational Resources Information Center

    Kamen, Gary

    2004-01-01

    During the earliest stages of resistance exercise training, initial muscular strength gains occur too rapidly to be explained solely by muscle-based mechanisms. However, increases in surface-based EMG amplitude as well as motor unit discharge rate provide some insight to the existence of neural mechanisms in the earliest phases of resistance…

  17. A Neural Auto-depth Controller for an Unmanned Underwater Vehicle

    NASA Astrophysics Data System (ADS)

    Sutton, R.; Johnson, C.; Roberts, G. N.

    Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVS). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The results show that, for differing sized MLPs, the chemotaxis algorithm produces a successful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle mass, the neural controller out-performed a classical proportional-integral-derivative controller.

  18. Controlling the elements: an optogenetic approach to understanding the neural circuits of fear.

    PubMed

    Johansen, Joshua P; Wolff, Steffen B E; Lüthi, Andreas; LeDoux, Joseph E

    2012-06-15

    Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatomically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior.

  19. The Ca2+-induced methyltransferase xPRMT1b controls neural fate in amphibian embryo.

    PubMed

    Batut, Julie; Vandel, Laurence; Leclerc, Catherine; Daguzan, Christiane; Moreau, Marc; Néant, Isabelle

    2005-10-18

    We have previously shown that an increase in intracellular Ca2+ is both necessary and sufficient to commit ectoderm to a neural fate in Xenopus embryos. However, the relationship between this Ca2+ increase and the expression of early neural genes has yet to be defined. Using a subtractive cDNA library between untreated and caffeine-treated animal caps, i.e., control ectoderm and ectoderm induced toward a neural fate by a release of Ca2+, we have isolated the arginine N-methyltransferase, xPRMT1b, a Ca2+-induced target gene, which plays a pivotal role in this process. First, we show in embryo and in animal cap that xPRMT1b expression is Ca2+-regulated. Second, overexpression of xPRMT1b induces the expression of early neural genes such as Zic3. Finally, in the whole embryo, antisense approach with morpholino oligonucleotide against xPRMT1b impairs neural development and in animal caps blocks the expression of neural markers induced by a release of internal Ca2+. Our results implicate an instructive role of an enzyme, an arginine methyltransferase protein, in the embryonic choice of determination between epidermal and neural fate. The results presented provide insights by which a Ca2+ increase induces neural fate.

  20. Imaging the neural circuitry and chemical control of aggressive motivation

    PubMed Central

    Ferris, Craig F; Stolberg, Tara; Kulkarni, Praveen; Murugavel, Murali; Blanchard, Robert; Blanchard, D Caroline; Febo, Marcelo; Brevard, Mathew; Simon, Neal G

    2008-01-01

    Background With the advent of functional magnetic resonance imaging (fMRI) in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus), emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex) and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding. Drugs that block vasopressin

  1. Multipotent adult hippocampal progenitor cells maintained as neurospheres favor differentiation toward glial lineages

    PubMed Central

    Oh, Jisun; Daniels, Gabrielle J.; Chiou, Lawrence S.; Ye, Eun-Ah; Jeong, Yong-Seob; Sakaguchi, Donald S.

    2014-01-01

    Adult hippocampal progenitor cells (AHPCs) are generally maintained as a dispersed monolayer population of multipotent neural progenitors. To better understand cell-cell interactions among neural progenitors and their influences on cellular characteristics, we generated free-floating cellular aggregates, or neurospheres, from the adherent monolayer population of AHPCs. Results from in vitro analyses demonstrated that both populations of AHPCs were highly proliferative under maintenance conditions, but AHPCs formed in neurospheres favored differentiation along a glial lineage and displayed greater migrational activity, than the traditionally cultured AHPCs. To study the plasticity of AHPCs from both populations in vivo, we transplanted GFP-expressing AHPCs via intraocular injection into the developing rat eyes. Both AHPC populations were capable of surviving and integrating into the developing host central nervous system, but considerably more GFP-positive cells were observed in the retinas transplanted with neurosphere AHPCs, compared to adherent AHPCs. These results suggest that the culture configuration during maintenance for neural progenitor cells (NPCs) influences cell fate and motility in vitro as well as in vivo. Our findings have implication for understanding different cellular characteristics of NPCs according to distinct intercellular architectures and for developing cell-based therapeutic strategies using lineage-committed NPCs. PMID:24844209

  2. Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic.

    PubMed

    Chen, Yu-Luen; Chen, Shih-Ching; Chen, Weoi-Luen; Hsiao, Chin-Chih; Kuo, Te-Son; Lai, Jin-Shin

    2004-01-01

    This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.

  3. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    PubMed Central

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2013-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of attentional control. Our results indicated neural dysregulation across a wide range of brain regions including those involved in overall arousal, top-down attentional control, late-stage and response selection and inhibition. Furthermore, this dysregulation was most notable in lateral regions of DLPFC for sustained attentional control and in medial areas for transient aspects of attentional control. Because of the careful selection and matching of our two groups, these results provide strong evidence that the neural systems of attentional control are dysregulated in young adults with ADHD and are similar to dysregulations seen in children and adolescents with ADHD. PMID:19619566

  4. Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo.

    PubMed

    Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A; Song, Junfang; Halley, Pamela A; Das, Raman M; Burt, Dave W; Barton, Geoffrey J; Storey, Kate G

    2014-08-01

    Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. PMID:25063452

  5. Evaluating the performances of statistical and neural network based control charts

    NASA Astrophysics Data System (ADS)

    Teoh, Kok Ban; Ong, Hong Choon

    2015-10-01

    Control chart is used widely in many fields and traditional control chart is no longer adequate in detecting a sudden change in a particular process. So, run rules which are built in into Shewhart X ¯ control chart while Exponential Weighted Moving Average control chart (EWMA), Cumulative Sum control chart (CUSUM) and neural network based control chart are introduced to overcome the limitation regarding to the sensitivity of traditional control chart. In this study, the average run length (ARL) and median run length (MRL) in the shifts in the process mean of control charts mentioned will be computed. We will show that interpretations based only on the ARL can be misleading. Thus, MRL is also used to evaluate the performances of the control charts. From this study, neural network based control chart is found to possess a better performance than run rules of Shewhart X ¯ control chart, EWMA and CUSUM control chart.

  6. Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

    PubMed Central

    Pedrocchi, Alessandra; Ferrante, Simona; De Momi, Elena; Ferrigno, Giancarlo

    2006-01-01

    Background The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. Methods The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. Results The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. Conclusion Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice. PMID:17029636

  7. A neural manufacturing a novel concept for processing modeling, monitoring and control

    SciTech Connect

    Law, B.; Fu, C.Y.; Petrich, L.

    1995-10-01

    Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.

  8. Neural network for solving Nash equilibrium problem in application of multiuser power control.

    PubMed

    He, Xing; Yu, Junzhi; Huang, Tingwen; Li, Chuandong; Li, Chaojie

    2014-09-01

    In this paper, based on an equivalent mixed linear complementarity problem, we propose a neural network to solve multiuser power control optimization problems (MPCOP), which is modeled as the noncooperative Nash game in modern digital subscriber line (DSL). If the channel crosstalk coefficients matrix is positive semidefinite, it is shown that the proposed neural network is stable in the sense of Lyapunov and global convergence to a Nash equilibrium, and the Nash equilibrium is unique if the channel crosstalk coefficients matrix is positive definite. Finally, simulation results on two numerical examples show the effectiveness and performance of the proposed neural network.

  9. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

    PubMed

    Yang, Shiju; Li, Chuandong; Huang, Tingwen

    2016-03-01

    The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.

  10. Parameter estimation and control for a neural mass model based on the unscented Kalman filter

    NASA Astrophysics Data System (ADS)

    Liu, Xian; Gao, Qing

    2013-10-01

    Recent progress in Kalman filters to estimate states and parameters in nonlinear systems has provided the possibility of applying such approaches to neural systems. We here apply the nonlinear method of unscented Kalman filters (UKFs) to observe states and estimate parameters in a neural mass model that can simulate distinct rhythms in electroencephalography (EEG) including dynamical evolution during epilepsy seizures. We demonstrate the efficiency of the UKF in estimating states and parameters. We also develop an UKF-based control strategy to modulate the dynamics of the neural mass model. In this strategy the UKF plays the role of observing states, and the control law is constructed via the estimated states. We demonstrate the feasibility of using such a strategy to suppress epileptiform spikes in the neural mass model.

  11. An input-output based robust stabilization criterion for neural-network control of nonlinear systems.

    PubMed

    Fernández de Cañete, J; Barreiro, A; García-Cerezo, A; García-Moral, I

    2001-01-01

    A stabilization method based on the input-output conicity criterion is presented. Conventional learning algorithms are applied to adjust the controller dynamics, and robust stability of the closed-loop system is guaranteed by modifying the training patterns which yield unstable behavior. The methodology developed expands the class of nonlinear systems to be controlled using neural control schemes, so that the stabilization of a broad class of neural-network-based control systems, even with unknown dynamics, is assured. Straightforwardness in the application of this method is evident in contrast to the Lyapunov function approach. PMID:18249978

  12. Dynamic control of ROV`s making use of the neural network concept

    SciTech Connect

    Ooi, Tadashi; Yoshida, Yuki; Takahashi, Yoshiaki; Kidoushi, Hideki

    1994-12-31

    An attempt is made to combine the classical controller with the concept of neural network, the result of which is a control system that they have named the Robust Adaptive Neural-net Controller (RANC). The RANC identifies the dynamic characteristics of the remotely operated vehicle (ROV) including its ambient environment involving cyclic disturbances such as forces induced by waves, and organizes automatically an optimized controller. A tank experiment is described in which the RANC is set to maintain a model ROV at a prescribed depth of water under artificially generated wave disturbance.

  13. Neural Control of Motion-to-Force Transitions with the Fingertip

    PubMed Central

    Venkadesan, Madhusudhan; Valero-Cuevas, Francisco J.

    2010-01-01

    The neural control of tasks such as rapid acquisition of precision pinch remains unknown. Therefore, we investigated the neural control of finger musculature when the index fingertip abruptly transitions from motion to static force production. Nine subjects produced a downward tapping motion followed by vertical fingertip force against a rigid surface. We simultaneously recorded three-dimensional fingertip force, plus the complete muscle coordination pattern using intramuscular electromyograms from all seven index finger muscles. We found that the muscle coordination pattern clearly switched from that for motion to that for isometric force ~5 ms before contact (p = 0.0004). Mathematical modeling and analysis revealed that the underlying neural control also switched between mutually incompatible strategies in a time-critical manner. Importantly, this abrupt switch in underlying neural control polluted fingertip force vector direction beyond what is explained by muscle activation-contraction dynamics and neuromuscular noise (p ≤0.003). We further ruled out an impedance control strategy in a separate test showing no systematic change in initial force magnitude for catch trials where the tapping surface was surreptitiously lowered and raised (p = 0.93). We conclude that the nervous system predictively switches between mutually incompatible neural control strategies to bridge the abrupt transition in mechanical constraints between motion and static force. Moreover because the nervous system cannot switch between control strategies instantaneously or exactly, there arise physical limits to the accuracy of force production on contact. The need for such a neurally demanding and time-critical strategy for routine motion-to-force transitions with the fingertip may explain the existence of specialized neural circuits for the human hand. PMID:18256256

  14. Effects of hypoxia on sympathetic neural control in humans

    NASA Technical Reports Server (NTRS)

    Smith, M. L.; Muenter, N. K.

    2000-01-01

    This special issue is principally focused on the time domain of the adaptive mechanisms of ventilatory responses to short-term, long-term and intermittent hypoxia. The purpose of this review is to summarize the limited literature on the sympathetic neural responses to sustained or intermittent hypoxia in humans and attempt to discern the time domain of these responses and potential adaptive processes that are evoked during short and long-term exposures to hypoxia.

  15. Real-time water treatment process control with artificial neural networks

    SciTech Connect

    Zhang, Q.; Stanley, S.J.

    1999-02-01

    With more stringent requirements being placed on water treatment performance, operators need a reliable tool to optimize the process control in the treatment plant. In the present paper, one such tool is presented, which is a process control system built with the artificial neural network (ANN) modeling approach. The coagulation, flocculation, and sedimentation processes involve many complex physical and chemical phenomena and thus are difficult to model for process control with traditional methods. Proposed is the use of a neural network process control system for the coagulation, flocculation, and sedimentation processes. Presented is a review of influential control parameters and control requirements for these processes followed by the development of a feed forward neural network control scheme. A neural network process model was built based on nearly 2,000 sets of process control data. This model formed the major component of a software controller and was found to consistently predict the optimum alum and power activated carbon doses for different control actions. With minor modifications, the approach illustrated can be used for building control models for other water treatment processes.

  16. Controlling chaos in balanced neural circuits with input spike trains

    NASA Astrophysics Data System (ADS)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  17. Predictive and Neural Predictive Control of Uncertain Systems

    NASA Technical Reports Server (NTRS)

    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.

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

    PubMed

    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.

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

    PubMed

    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. PMID:26259222

  20. Impulsive control of stochastic systems with applications in chaos control, chaos synchronization, and neural networks.

    PubMed

    Li, Chunguang; Chen, Luonan; Aihara, Kazuyuki

    2008-06-01

    Real systems are often subject to both noise perturbations and impulsive effects. In this paper, we study the stability and stabilization of systems with both noise perturbations and impulsive effects. In other words, we generalize the impulsive control theory from the deterministic case to the stochastic case. The method is based on extending the comparison method to the stochastic case. The method presented in this paper is general and easy to apply. Theoretical results on both stability in the pth mean and stability with disturbance attenuation are derived. To show the effectiveness of the basic theory, we apply it to the impulsive control and synchronization of chaotic systems with noise perturbations, and to the stability of impulsive stochastic neural networks. Several numerical examples are also presented to verify the theoretical results.

  1. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

    PubMed

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  2. Functional Multipotency of Stem Cells: A Conceptual Review of Neurotrophic Factor-Based Evidence and Its Role in Translational Research

    PubMed Central

    Teng, Yang D; Yu, Dou; Ropper, Alexander E; Li, Jianxue; Kabatas, Serdar; Wakeman, Dustin R; Wang, Junmei; Sullivan, Maryrose P; Redmond, D. Eugene; Langer, Robert; Snyder, Evan Y; Sidman, Richard L

    2011-01-01

    We here propose an updated concept of stem cells (SCs), with an emphasis on neural stem cells (NSCs). The conventional view, which has touched principally on the essential property of lineage multipotency (e.g., the ability of NSCs to differentiate into all neural cells), should be broadened to include the emerging recognition of biofunctional multipotency of SCs to mediate systemic homeostasis, evidenced in NSCs in particular by the secretion of neurotrophic factors. Under this new conceptual context and taking the NSC as a leading example, one may begin to appreciate and seek the “logic” behind the wide range of molecular tactics the NSC appears to serve at successive developmental stages as it integrates into and prepares, modifies, and guides the surrounding CNS micro- and macro-environment towards the formation and self-maintenance of a functioning adult nervous system. We suggest that embracing this view of the “multipotency” of the SCs is pivotal for correctly, efficiently, and optimally exploiting stem cell biology for therapeutic applications, including reconstitution of a dysfunctional CNS. PMID:22654717

  3. Spatial and temporal control of cell aggregation efficiently directs human pluripotent stem cells towards neural commitment.

    PubMed

    Miranda, Cláudia C; Fernandes, Tiago G; Pascoal, Jorge F; Haupt, Simone; Brüstle, Oliver; Cabral, Joaquim M S; Diogo, Maria Margarida

    2015-10-01

    3D suspension culture is generally considered a promising method to achieve efficient expansion and controlled differentiation of human pluripotent stem cells (hPSCs). In this work, we focused on developing an integrated culture platform for expansion and neural commitment of hPSCs into neural precursors using 3D suspension conditions and chemically-defined culture media. We evaluated different inoculation methodologies for hPSC expansion as 3D aggregates and characterized the resulting cultures in terms of aggregate size distribution. It was demonstrated that upon single-cell inoculation, after four days of culture, 3D aggregates were composed of homogenous populations of hPSC and were characterized by an average diameter of 139 ± 26 μm, which was determined to be the optimal size to initiate neural commitment. Temporal analysis revealed that upon neural specification it is possible to maximize the percentage of neural precursor cells expressing the neural markers Sox1 and Pax6 after nine days of culture. These results highlight our ability to define a robust method for production of hPSC-derived neural precursors that minimizes processing steps and that constitutes a promising alternative to the traditional planar adherent culture system due to a high potential for scaling-up. PMID:25866360

  4. Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions.

    PubMed

    Zhang, Tianping; Ge, Shuzhi Sam

    2009-03-01

    In this paper, adaptive neural network (NN) tracking control is investigated for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions. The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown control directions. It is shown that the dead-zone output can be represented as a simple linear system with a static time-varying gain and bounded disturbance by introducing characteristic function. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, with tracking errors converging to zero under the condition that the slopes of unknown dead zones are equal. Simulation results demonstrate the effectiveness of the approach.

  5. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    PubMed

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  6. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

    NASA Technical Reports Server (NTRS)

    Burken, John J.

    2007-01-01

    This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

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

    NASA Astrophysics Data System (ADS)

    Podolski, Ina; Rettberg, Achim

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

  8. Quasi-Newton training in supervised neural networks: An application to process control

    SciTech Connect

    Ferrigno, S.; Achenie, L.

    1996-12-31

    In recent years, neural networks have found uses in chemical process control, modeling and diagnosis. In spite of the promise that these neural network paradigms hold, there are several challenges we often face when using these neural networks. These include but are not limited to (1) how fast training can be achieved and (2) how to pick the network topology. In the oral presentation, I will discuss the use of Newton-type strategies and parallel computing to address the first issue. In this paper, however, a quasi-Newton training strategy is used to carry out control of the liquid level in a serially linked draining tank problem. It has been demonstrated that a neuro-controller (trained off-line) performs at least as well as the PID family of controllers on laboratory scale sequential draining tanks. 5 refs., 8 figs.

  9. Neural network controller development for a magnetically suspended flywheel energy storage system

    NASA Technical Reports Server (NTRS)

    Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.

    1994-01-01

    A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

  10. Vive les differences! Individual variation in neural mechanisms of executive control

    PubMed Central

    Braver, Todd S.; Cole, Michael W.; Yarkoni, Tal

    2010-01-01

    Summary Investigations of individual differences have become increasingly important in the cognitive neuroscience of executive control. For instance, individual variation in lateral prefrontal cortex function (and that of associated regions) has recently been used to identify contributions of executive control processes to a number of domains, including working memory capacity, anxiety, reward/motivation, and emotion regulation. However, the origins of such individual differences remain poorly understood. Recent progress toward identifying the genetic and environmental sources of variation in neural traits, in combination with progress identifying the causal relationships between neural and cognitive processes, will be essential for developing a mechanistic understanding of executive control. PMID:20381337

  11. Adaptive neural control for a class of nonlinearly parametric time-delay systems.

    PubMed

    Ho, Daniel W C; Li, Junmin; Niu, Yugang

    2005-05-01

    In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.

  12. Neural-network-based speed controller for induction motors using inverse dynamics model

    NASA Astrophysics Data System (ADS)

    Ahmed, Hassanein S.; Mohamed, Kamel

    2016-08-01

    Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

  13. Characteristics and multipotency of equine dedifferentiated fat cells.

    PubMed

    Murata, Daiki; Yamasaki, Atsushi; Matsuzaki, Shouta; Sunaga, Takafumi; Fujiki, Makoto; Tokunaga, Satoshi; Misumi, Kazuhiro

    2016-01-01

    Dedifferentiated fat (DFAT) cells have been shown to be multipotent, similar to mesenchymal stem cells (MSCs). In this study, we aimed to establish and characterize equine DFAT cells. Equine adipocytes were ceiling cultured, and then dedifferentiated into DFAT cells by the seventh day of culture. The number of DFAT cells was increased to over 10 million by the fourth passage. Flow cytometry of DFAT cells showed that the cells were strongly positive for CD44, CD90, and major histocompatibility complex (MHC) class I; moderately positive for CD11a/18, CD105, and MHC class II; and negative for CD34 and CD45. Moreover, DFAT cells were positive for the expression of sex determining region Y-box 2 as a marker of multipotency. Finally, we found that DFAT cells could differentiate into osteogenic, chondrogenic, and adipogenic lineages under specific nutrient conditions. Thus, DFAT cells could have clinical applications in tissue regeneration, similar to MSCs derived from adipose tissue.

  14. Neural circuits controlling behavior and autonomic functions in medicinal leeches.

    PubMed

    Lamb, Damon G; Calabrese, Ronald L

    2011-09-28

    In the study of the neural circuits underlying behavior and autonomic functions, the stereotyped and accessible nervous system of medicinal leeches, Hirudo sp., has been particularly informative. These leeches express well-defined behaviors and autonomic movements which are amenable to investigation at the circuit and neuronal levels. In this review, we discuss some of the best understood of these movements and the circuits which underlie them, focusing on swimming, crawling and heartbeat. We also discuss the rudiments of decision-making: the selection between generally mutually exclusive behaviors at the neuronal level.

  15. A BMP regulatory network controls ectodermal cell fate decisions at the neural plate border.

    PubMed

    Reichert, Sabine; Randall, Rebecca A; Hill, Caroline S

    2013-11-01

    During ectodermal patterning the neural crest and preplacodal ectoderm are specified in adjacent domains at the neural plate border. BMP signalling is required for specification of both tissues, but how it is spatially and temporally regulated to achieve this is not understood. Here, using a transgenic zebrafish BMP reporter line in conjunction with double-fluorescent in situ hybridisation, we show that, at the beginning of neurulation, the ventral-to-dorsal gradient of BMP activity evolves into two distinct domains at the neural plate border: one coinciding with the neural crest and the other abutting the epidermis. In between is a region devoid of BMP activity, which is specified as the preplacodal ectoderm. We identify the ligands required for these domains of BMP activity. We show that the BMP-interacting protein Crossveinless 2 is expressed in the BMP activity domains and is under the control of BMP signalling. We establish that Crossveinless 2 functions at this time in a positive-feedback loop to locally enhance BMP activity, and show that it is required for neural crest fate. We further demonstrate that the Distal-less transcription factors Dlx3b and Dlx4b, which are expressed in the preplacodal ectoderm, are required for the expression of a cell-autonomous BMP inhibitor, Bambi-b, which can explain the specific absence of BMP activity in the preplacodal ectoderm. Taken together, our data define a BMP regulatory network that controls cell fate decisions at the neural plate border.

  16. Conserved roles for Oct4 homologues in maintaining multipotency during early vertebrate development.

    PubMed

    Morrison, Gillian M; Brickman, Joshua M

    2006-05-01

    All vertebrate embryos have multipotent cells until gastrulation but, to date, derivation of embryonic stem (ES) cell lines has been achieved only for mouse and primates. ES cells are derived from mammalian inner cell mass (ICM) tissue that express the Class V POU domain (PouV) protein Oct4. Loss of Oct4 in mice results in a failure to maintain ICM and consequently an inability to derive ES cells. Here, we show that Oct4 homologues also function in early amphibian development where they act as suppressors of commitment during germ layer specification. Antisense morpholino mediated PouV knockdown in Xenopus embryos resulted in severe posterior truncations and anterior neural defects. Gastrulation stage embryos showed reduced expression of genes associated with uncommitted marginal zone cells, while the expression of markers associated with more mature cell states was expanded. Importantly, we have tested PouV proteins from a number of vertebrate species for the ability to substitute Oct4 in mouse ES cells. PouV domain proteins from both Xenopus and axolotl could support murine ES cell self-renewal but the only identified zebrafish protein in this family could not. Moreover, we found that PouV proteins regulated similar genes in ES cells and Xenopus embryos, and that PouV proteins capable of supporting ES cell self-renewal could also rescue the Xenopus PouV knockdown phenotype. We conclude that the unique ability of Oct4 to maintain ES cell pluripotency is derived from an ancestral function of this class of proteins to maintain multipotency. PMID:16651543

  17. Functional Characteristics of Multipotent Mesenchymal Stromal Cells from Pituitary Adenomas.

    PubMed

    Megnis, Kaspars; Mandrika, Ilona; Petrovska, Ramona; Stukens, Janis; Rovite, Vita; Balcere, Inga; Jansone, Laima Sabine; Peculis, Raitis; Pirags, Valdis; Klovins, Janis

    2016-01-01

    Pituitary adenomas are one of the most common endocrine and intracranial neoplasms. Although they are theoretically monoclonal in origin, several studies have shown that they contain different multipotent cell types that are thought to play an important role in tumor initiation, maintenance, and recurrence after therapy. In the present study, we isolated and characterized cell populations from seven pituitary somatotroph, nonhormonal, and lactotroph adenomas. The obtained cells showed characteristics of multipotent mesenchymal stromal cells as observed by cell morphology, cell surface marker CD90, CD105, CD44, and vimentin expression, as well as differentiation to osteogenic and adipogenic lineages. They are capable of growth and passaging under standard laboratory cell culture conditions and do not manifest any hormonal cell characteristics. Multipotent mesenchymal stromal cells are present in pituitary adenomas regardless of their clinical manifestation and show no considerable expression of somatostatin 1-5 and dopamine 2 receptors. Most likely obtained cells are a part of tissue-supportive cells in pituitary adenoma microenvironment. PMID:27340409

  18. Functional Characteristics of Multipotent Mesenchymal Stromal Cells from Pituitary Adenomas

    PubMed Central

    Megnis, Kaspars; Mandrika, Ilona; Petrovska, Ramona; Stukens, Janis; Rovite, Vita; Balcere, Inga; Jansone, Laima Sabine; Peculis, Raitis; Pirags, Valdis

    2016-01-01

    Pituitary adenomas are one of the most common endocrine and intracranial neoplasms. Although they are theoretically monoclonal in origin, several studies have shown that they contain different multipotent cell types that are thought to play an important role in tumor initiation, maintenance, and recurrence after therapy. In the present study, we isolated and characterized cell populations from seven pituitary somatotroph, nonhormonal, and lactotroph adenomas. The obtained cells showed characteristics of multipotent mesenchymal stromal cells as observed by cell morphology, cell surface marker CD90, CD105, CD44, and vimentin expression, as well as differentiation to osteogenic and adipogenic lineages. They are capable of growth and passaging under standard laboratory cell culture conditions and do not manifest any hormonal cell characteristics. Multipotent mesenchymal stromal cells are present in pituitary adenomas regardless of their clinical manifestation and show no considerable expression of somatostatin 1–5 and dopamine 2 receptors. Most likely obtained cells are a part of tissue-supportive cells in pituitary adenoma microenvironment. PMID:27340409

  19. Neural network for quality control of submunitions produced by injection loading

    SciTech Connect

    Smith, R.E.; Parkinson, W.J.; Hinde, R.F. Jr.; Wantuck, P.J.; Newman, K.E.

    1998-12-01

    Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing.

  20. Some neural correlates of sensorial and cognitive control of behavior

    NASA Astrophysics Data System (ADS)

    Ogmen, Haluk; Prakash, R. V.; Moussa, M.

    1992-07-01

    Development and maintenance of unsupervised intelligent activity relies on an active interaction with the environment. Such active exploratory behavior plays an essential role in both the development and adult phases of higher biological systems including humans. Exploration initiates a self-organization process whereby a coherent fusion of different sensory and motor modalities can be achieved (sensory-motor development) and maintained (adult rearrangement). In addition, the development of intelligence depends critically on an active manipulation of the environment. These observations are in sharp contrast with current attempts of artificial intelligence and various neural network models. In this paper, we present a neural network model that combines internal drives and environmental cues to reach behavioral decisions for the exploratory activity. The vision system consists of an ambient and a focal system. The ambient vision system guides eye movements by using nonassociative learning. This sensory based attentional focusing is augmented by a `cognitive' system using models developed for various aspects of frontal lobe function. The combined system has nonassociative learning, reinforcement learning, selective attention, habit formation, and flexible criterion categorization properties.

  1. Axonal control of the adult neural stem cell niche.

    PubMed

    Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D; Tecott, Laurence H; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

    2014-04-01

    The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSCs) in the walls of the lateral ventricles of the adult brain. How the adult brain's neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

  2. Axonal Control of the Adult Neural Stem Cell Niche

    PubMed Central

    Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D.; Tecott, Laurence H.; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

    2014-01-01

    SUMMARY The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSC) in the walls of the lateral ventricles of the adult brain. How the adult brain’s neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

  3. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    PubMed

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  4. Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

    PubMed Central

    Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

  5. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    PubMed

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  6. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    PubMed Central

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine

    2008-01-01

    Purpose To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural plasticity and discusses possible similarities and differences for application to speech motor control disorders. The possible involvement of neural plasticity in changes in speech production in normalcy, development, aging, and neurological diseases and disorders was considered. This report focuses on the appropriate use of functional and structural neuroimaging and the design of feasibility studies aimed at understanding how brain mechanisms are altered by environmental manipulations such as training and stimulation and how these changes might enhance the future development of rehabilitative methods for persons with speech motor control disorders. Conclusions Increased collaboration with neuroscientists working in clinical research centers addressing human communication disorders might foster research in this area. It is hoped that this paper will encourage future research on speech motor control disorders to address the principles of neural plasticity and their application for rehabilitation. PMID:18230849

  7. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    PubMed Central

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  8. Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

    PubMed

    Khader, Patrick H; Pachur, Thorsten; Weber, Lilian A E; Jost, Kerstin

    2016-01-01

    Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

  9. Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

    NASA Astrophysics Data System (ADS)

    Ekkachai, Kittipong; Tungpimolrut, Kanokvate; Nilkhamhang, Itthisek

    2013-11-01

    An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption.

  10. Adaptive neural network nonlinear control for BTT missile based on the differential geometry method

    NASA Astrophysics Data System (ADS)

    Wu, Hao; Wang, Yongji; Xu, Jiangsheng

    2007-11-01

    A new nonlinear control strategy incorporated the differential geometry method with adaptive neural networks is presented for the nonlinear coupling system of Bank-to-Turn missile in reentry phase. The basic control law is designed using the differential geometry feedback linearization method, and the online learning neural networks are used to compensate the system errors due to aerodynamic parameter errors and external disturbance in view of the arbitrary nonlinear mapping and rapid online learning ability for multi-layer neural networks. The online weights and thresholds tuning rules are deduced according to the tracking error performance functions by Levenberg-Marquardt algorithm, which will make the learning process faster and more stable. The six degree of freedom simulation results show that the attitude angles can track the desired trajectory precisely. It means that the proposed strategy effectively enhance the stability, the tracking performance and the robustness of the control system.

  11. A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network

    PubMed Central

    Tan, Chao; Qi, Nan; Yao, Xingang; Wang, Zhongbin; Si, Lei

    2015-01-01

    In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others. PMID:25861253

  12. Generation of the Human Biped Stance by a Neural Controller Able to Compensate Neurological Time Delay

    PubMed Central

    Jiang, Ping; Chiba, Ryosuke; Takakusaki, Kaoru; Ota, Jun

    2016-01-01

    The development of a physiologically plausible computational model of a neural controller that can realize a human-like biped stance is important for a large number of potential applications, such as assisting device development and designing robotic control systems. In this paper, we develop a computational model of a neural controller that can maintain a musculoskeletal model in a standing position, while incorporating a 120-ms neurological time delay. Unlike previous studies that have used an inverted pendulum model, a musculoskeletal model with seven joints and 70 muscular-tendon actuators is adopted to represent the human anatomy. Our proposed neural controller is composed of both feed-forward and feedback controls. The feed-forward control corresponds to the constant activation input necessary for the musculoskeletal model to maintain a standing posture. This compensates for gravity and regulates stiffness. The developed neural controller model can replicate two salient features of the human biped stance: (1) physiologically plausible muscle activations for quiet standing; and (2) selection of a low active stiffness for low energy consumption. PMID:27655271

  13. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    PubMed

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations. PMID:26321943

  14. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

    PubMed Central

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations. PMID:26321943

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

    NASA Technical Reports Server (NTRS)

    Williams, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. 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 the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights 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 a pitch frequency sweep and an 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. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

  16. Stem cell biology is population biology: differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors

    PubMed Central

    2013-01-01

    The hematopoietic stem cell (HSC) system is a demand control system, with the demand coming from the organism, since the products of the common myeloid and lymphoid progenitor (CMP, CLP respectively) cells are essential for activity and defense against disease. We show how ideas from population biology (combining population dynamics and evolutionary considerations) can illuminate the feedback control of the HSC system by the fully differentiated products, which has recently been verified experimentally. We develop models for the penultimate differentiation of HSC Multipotent Progenitors (MPPs) into CLP and CMP and introduce two concepts from population biology into stem cell biology. The first concept is the Multipotent Progenitor Commitment Response (MPCR) which is the probability that a multipotent progenitor cell follows a CLP route rather than a CMP route. The second concept is the link between the MPCR and a measure of Darwinian fitness associated with organismal performance and the levels of differentiated lymphoid and myeloid cells. We show that many MPCRs are consistent with homeostasis, but that they will lead to different dynamics of cells and signals following a wound or injury and thus have different consequences for Darwinian fitness. We show how coupling considerations of life history to dynamics of the HSC system and its products allows one to compute the selective pressures on cellular processes. We discuss ways that this framework can be used and extended. PMID:23327512

  17. On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed

    1996-01-01

    A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

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

    PubMed

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

    2014-01-01

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

  19. The neural selection and control of saccades by the frontal eye field.

    PubMed Central

    Schall, Jeffrey D

    2002-01-01

    Recent research has provided new insights into the neural processes that select the target for and control the production of a shift of gaze. Being a key node in the network that subserves visual processing and saccade production, the frontal eye field (FEF) has been an effective area in which to monitor these processes. Certain neurons in the FEF signal the location of conspicuous or meaningful stimuli that may be the targets for saccades. Other neurons control whether and when the gaze shifts. The existence of distinct neural processes for visual selection and saccade production is necessary to explain the flexibility of visually guided behaviour. PMID:12217175

  20. Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control.

    PubMed

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; Xiao, Mingqing

    2015-11-01

    In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.

  1. Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control.

    PubMed

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; Xiao, Mingqing

    2015-11-01

    In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper. PMID:26319051

  2. Using Neural Networks to Explore Air Traffic Controller Workload

    NASA Technical Reports Server (NTRS)

    Martin, Lynne; Kozon, Thomas; Verma, Savita; Lozito, Sandra C.

    2006-01-01

    When a new system, concept, or tool is proposed in the aviation domain, one concern is the impact that this will have on operator workload. As an experience, workload is difficult to measure in a way that will allow comparison of proposed systems with those already in existence. Chatterji and Sridhar (2001) suggested a method by which airspace parameters can be translated into workload ratings, using a neural network. This approach was employed, and modified to accept input from a non-real time airspace simulation model. The following sections describe the preparations and testing work that will enable comparison of a future airspace concept with a current day baseline in terms of workload levels.

  3. Combined optical tweezers and laser dissector for controlled ablation of functional connections in neural networks

    NASA Astrophysics Data System (ADS)

    Difato, Francesco; Dal Maschio, Marco; Marconi, Emanuele; Ronzitti, Giuseppe; Maccione, Alessandro; Fellin, Tommasso; Berdondini, Luca; Chieregatti, Evelina; Benfenati, Fabio; Blau, Axel

    2011-05-01

    Regeneration of functional connectivity within a neural network after different degrees of lesion is of utmost clinical importance. To test pharmacological approaches aimed at recovering from a total or partial damage of neuronal connections within a circuit, it is necessary to develop a precise method for controlled ablation of neuronal processes. We combined a UV laser microdissector to ablate neural processes in vitro at single neuron and neural network level with infrared holographic optical tweezers to carry out force spectroscopy measurements. Simultaneous force spectroscopy, down to the sub-pico-Newton range, was performed during laser dissection to quantify the tension release in a partially ablated neurite. Therefore, we could control and measure the damage inflicted to an individual neuronal process. To characterize the effect of the inflicted injury on network level, changes in activity of neural subpopulations were monitored with subcellular resolution and overall network activity with high temporal resolution by concurrent calcium imaging and microelectrode array recording. Neuronal connections have been sequentially ablated and the correlated changes in network activity traced and mapped. With this unique combination of electrophysiological and optical tools, neural activity can be studied and quantified in response to controlled injury at the subcellular, cellular, and network level.

  4. [The influence of extreme factors on homing multipotent mesenchymal stromal cells].

    PubMed

    Maklakova, I Yu; Grebnev, Y D; Yastrebov, A P

    2015-01-01

    In this study, we studied homing multipotent mesenchymal stromal cells under influence of extreme factors: after radiation exposure, acute blood loss. Absorbed dose ionizing radiation amounted to 4.0 C (causes acute radiation sickness in mice), acute blood loss was caused by bleeding from the tail vein of the mouse in the amount of 2% of the body weight of the animal. Label MMSC used fluorochrome DAPI, ready to use. The experiments were performed on 60 Mature mice (males) age 6-8 months, weighing 20-25 g. Experiments on the culture of multipotent mesenchymal stromal cells from the placenta (chorion) performed on laboratory mice female at the age of 3-4 months in the gestation period of 14 days. Introduction suspensions of MMSC was carried out at a dose of 6 million cells/mouse, suspended in 0.2 ml 0.9% NaCl solution. The control group of laboratory animals MMSC transplantation was carried out also in the amount of 6 million cells/mouse. The assessment was made of tissue chimerism in the peripheral blood, bone marrow, spleen, small intestine, liver, lung, kidney, heart after 1 and 24 hours after transplantation of labeled cells. It was found a significant decrease in the content of labeled MMSC in the peripheral blood at extreme impact, indicating a migration of the transplanted cells in the damaged tissue. Homing transplanted MMSC is realized mainly in those tissues that underwent the most damage. PMID:27116883

  5. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    PubMed

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. PMID:23041448

  6. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    PubMed

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network.

  7. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems.

    PubMed

    Vrabie, Draguna; Lewis, Frank

    2009-04-01

    In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.

  8. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems.

    PubMed

    Vrabie, Draguna; Lewis, Frank

    2009-04-01

    In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided. PMID:19362449

  9. Therapeutic effect of mesenchymal multipotent stromal cells on memory in animals with Alzheimer-type neurodegeneration.

    PubMed

    Bobkova, N V; Poltavtseva, R A; Samokhin, A N; Sukhikh, G T

    2013-11-01

    Transplantation of human mesenchymal multipotent stromal cells improved spatial memory in bulbectomized mice with Alzheimer-type neurodegeneration. The positive effect was observed in 1 month after intracerebral transplantation and in 3 months after systemic injection of mesenchymal multipotent stromal cells. No cases of malignant transformation were noted. These findings indicate prospects of using mesenchymal multipotent stromal cells for the therapy of Alzheimer disease and the possibility of their systemic administration for attaining the therapeutic effect.

  10. Control chart pattern recognition using K-MICA clustering and neural networks.

    PubMed

    Ebrahimzadeh, Ataollah; Addeh, Jalil; Rahmani, Zahra

    2012-01-01

    Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.

  11. Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks

    NASA Astrophysics Data System (ADS)

    Ait Abbas, Hamou; Belkheiri, Mohammed; Zegnini, Boubakeur

    2016-01-01

    We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor based on a modified field-oriented control method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modelling errors. The network weight adaptation rule is derived from the Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed-loop system. Computer simulations of an output feedback controlled induction machine, augmented via single-hidden-layer neural networks, demonstrate the practical potential of the proposed control algorithm.

  12. Artificial neural network based controller for permanent magnet DC motor drives

    SciTech Connect

    Hoque, M.A.; Zaman, M.R.; Rahman, M.A.

    1995-12-31

    This paper introduces a novel approach of designing a controller using multi-layer feed-forward neural network (FFNN) for the speed control of a permanent magnet (PM) dc motor. Artificial neural network (ANN) controller with its massive parallel properties and learning capabilities offers a promising way to solving the problem of system non-linearity, parameter variations and unexpected load excursions associated with a PM dc motor drive system. Self-tuning technique of the controller in real time is achieved through an improved on-line back-propagation training algorithm based on an output error propagation. The proposed ANN controller is implemented with a PM dc motor drive system in the laboratory. The laboratory test results validate the efficacy of the based controller for a high performance PM dc motor drive.

  13. Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

    SciTech Connect

    Meier, E.; Morgan, M. J.; Biedron, S. G.; LeBlanc, G.; Wu, J.

    2009-01-01

    This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.

  14. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

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

    PubMed

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

    2016-02-01

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

  16. Neural Network with Local Memory for Nuclear Reactor Power Level Control

    SciTech Connect

    Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri

    2001-02-15

    A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions.

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

    PubMed

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

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration. PMID:26336152

  18. Neuroscience: A Distributed Neural Network Controls REM Sleep.

    PubMed

    Peever, John; Fuller, Patrick M

    2016-01-11

    How does the brain control dreams? New science shows that a small node of cells in the medulla - the most primitive part of the brain - may function to control REM sleep, the brain state that underlies dreaming. PMID:26766231

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Design, stability and robustness analyses of neural networks in control systems

    NASA Astrophysics Data System (ADS)

    Shen, Jie

    1998-12-01

    Artificial Neural Network (ANN), also known as connectionist learning and parallel distributed processing, is finding its applications in diverse fields: many branches of engineering, health sciences, cognitive science, archaeology, finance, etc. This research tries to make some efforts to emphasize "design" methodology in ANN, and to explore the structures by which ANN can solve difficult problems by identifying proper ANN architecture. Two classes of ANN--multi-layer neural networks and recurrent networks--are investigated in the context of control of systems and estimation of unknown parameters. The multi-layer neural networks converge to optimal solutions by satisfying mathematical formulations associated with the Hamilton approach and the dynamic programming approach. A benchmark aerospace application is used for illustration. A variant of the Hopfield network, called the Modified Hopfield Neural Network (MHNN), is proposed to show the design approach to the determination of weights in recurrent networks. It is shown how the equilibrium point of this network helps with inversion operations arising in optimal gain determination. Control of dynamic systems using recurrent neural networks are presented. The robustness of the recurrent networks to parameter variation is considered in the context of weights. Analyses are carried out in the frequency domain and the time domain.

  1. Robust neural network motion tracking control of piezoelectric actuation systems for micro/nanomanipulation.

    PubMed

    Liaw, Hwee Choo; Shirinzadeh, Bijan; Smith, Julian

    2009-02-01

    This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.

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

    PubMed

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

    2014-08-01

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

  3. Embryonic cerebrospinal fluid in brain development: neural progenitor control.

    PubMed

    Gato, Angel; Alonso, M Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M F; Lamus, Francisco; Desmond, Mary E

    2014-08-28

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called "embryonic CSF." Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life. PMID:25165044

  4. Embryonic cerebrospinal fluid in brain development: neural progenitor control

    PubMed Central

    Gato, Angel; Alonso, M. Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M. F.; Lamus, Francisco; Desmond, Mary E.

    2014-01-01

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called “embryonic CSF.” Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life. PMID:25165044

  5. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-01-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  6. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-10-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  7. Embryonic cerebrospinal fluid in brain development: neural progenitor control.

    PubMed

    Gato, Angel; Alonso, M Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M F; Lamus, Francisco; Desmond, Mary E

    2014-08-28

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called "embryonic CSF." Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life.

  8. Synchronization for an array of neural networks with hybrid coupling by a novel pinning control strategy.

    PubMed

    Gong, Dawei; Lewis, Frank L; Wang, Liping; Xu, Ke

    2016-05-01

    In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results.

  9. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  10. Robust quasi-LPV control based on neural state-space models.

    PubMed

    Bendtsen, J D; Trangbaek, K

    2002-01-01

    We derive a synthesis result for robust linear parameter varying (LPV) output feedback controllers for nonlinear systems modeled by neural state-space models. This result is achieved by writing the neural state-space model on a linear fractional transformation (LFT) form in a nonconservative way, separating the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of linear matrix inequalities (LMIs) with added constraints, some implementation issues are addressed and a simulation example is presented.

  11. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    ERIC Educational Resources Information Center

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2009-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of…

  12. Neural Activation Underlying Cognitive Control in the Context of Neutral and Affectively Charged Pictures in Children

    ERIC Educational Resources Information Center

    Lamm, Connie; White, Lauren K.; McDermott, Jennifer Martin; Fox, Nathan A.

    2012-01-01

    The neural correlates of cognitive control for typically developing 9-year-old children were examined using dense-array ERPs and estimates of cortical activation (LORETA) during a go/no-go task with two conditions: a neutral picture condition and an affectively charged picture condition. Activation was estimated for the entire cortex after which…

  13. Neural Mechanisms of Interference Control and Time Discrimination in Attention-Deficit/Hyperactivity Disorder

    ERIC Educational Resources Information Center

    Vloet, Timo D.; Gilsbach, Susanne; Neufang, Susanne; Fink, Gereon R.; Herpertz-Dahlmann, Beate; Konrad, Kerstin

    2010-01-01

    Objective: Both executive functions and time perception are typically impaired in subjects with attention-deficit/hyperactivity disorder (ADHD). However, the exact neural mechanisms underlying these deficits remain to be investigated. Method: Fourteen subjects with ADHD and 14 age- and IQ-matched controls (aged 9 through 15 years) were assessed…

  14. Adaptive statistic tracking control based on two-step neural networks with time delays.

    PubMed

    Yi, Yang; Guo, Lei; Wang, Hong

    2009-03-01

    This paper presents a new type of control framework for dynamical stochastic systems, called statistic tracking control (STC). The system considered is general and non-Gaussian and the tracking objective is the statistical information of a given target probability density function (pdf), rather than a deterministic signal. The control aims at making the statistical information of the output pdfs to follow those of a target pdf. For such a control framework, a variable structure adaptive tracking control strategy is first established using two-step neural network models. Following the B-spline neural network approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. The dynamic neural network (DNN) is employed to identify the unknown nonlinear dynamics between the control input and the weights related to the integrated function. To achieve the required control objective, an adaptive controller based on the proposed DNN is developed so as to track a reference trajectory. Stability analysis for both the identification and tracking errors is developed via the use of Lyapunov stability criterion. Simulations are given to demonstrate the efficiency of the proposed approach. PMID:19179249

  15. Polyphenols: Multipotent Therapeutic Agents in Neurodegenerative Diseases

    PubMed Central

    Bhullar, Khushwant S.; Rupasinghe, H. P. Vasantha

    2013-01-01

    Aging leads to numerous transitions in brain physiology including synaptic dysfunction and disturbances in cognition and memory. With a few clinically relevant drugs, a substantial portion of aging population at risk for age-related neurodegenerative disorders require nutritional intervention. Dietary intake of polyphenols is known to attenuate oxidative stress and reduce the risk for related neurodegenerative diseases such as Alzheimer's disease (AD), stroke, multiple sclerosis (MS), Parkinson's disease (PD), and Huntington's disease (HD). Polyphenols exhibit strong potential to address the etiology of neurological disorders as they attenuate their complex physiology by modulating several therapeutic targets at once. Firstly, we review the advances in the therapeutic role of polyphenols in cell and animal models of AD, PD, MS, and HD and activation of drug targets for controlling pathological manifestations. Secondly, we present principle pathways in which polyphenol intake translates into therapeutic outcomes. In particular, signaling pathways like PPAR, Nrf2, STAT, HIF, and MAPK along with modulation of immune response by polyphenols are discussed. Although current polyphenol researches have limited impact on clinical practice, they have strong evidence and testable hypothesis to contribute clinical advances and drug discovery towards age-related neurological disorders. PMID:23840922

  16. Neural interfaces for upper-limb prosthesis control: opportunities to improve long-term reliability.

    PubMed

    Judy, Jack W

    2012-03-01

    Building on a long history of innovation in neural-recording interfaces, the Defense Advanced Research Projects Agency (DARPA) has launched a program to address the key challenges related to transitioning advanced neuroprosthesis technology to clinical use for amputated service members. The goal of the Reliable Neural Technology (RE-NET) Program is to develop new technology to extract information from the nervous system at a scale and rate needed to reliably control modern robotic prostheses over the lifetime of the amputee. The RE-NET program currently encompasses three separate efforts: histology for interface stability over time (HIST), reliable peripheral interfaces (RPIs), and reliable central nervous system (CNS) interfaces (RCIs).

  17. Information content of neural networks with self-control and variable activity

    NASA Astrophysics Data System (ADS)

    Bollé, D.; Amari, S. I.; Dominguez Carreta, D. R. C.; Massolo, G.

    2001-02-01

    A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations.

  18. Neural control of white, beige and brown adipocytes.

    PubMed

    Bartness, T J; Ryu, V

    2015-08-01

    Reports of brown-like adipocytes in traditionally white adipose tissue (WAT) depots occurred ~30 years ago, but interest in white adipocyte 'browning' only has gained attention more recently. We integrate some of what is known about the sympathetic nervous system (SNS) innervation of WAT and brown adipose tissue (BAT) with the few studies focusing on the sympathetic innervation of the so-called 'brite' or 'beige' adipocytes that appear when WAT sympathetic drive increases (for example, cold exposure and food deprivation). Only one brain site, the dorsomedial hypothalamic nucleus (DMH), selectively browns some (inguinal WAT (IWAT) and dorsomedial subcutaneous WAT), but not all WAT depots and only when DMH neuropeptide Y gene expression is knocked down, a browning effect is mediated by WAT SNS innervation. Other studies show that WAT sympathetic fiber density is correlated with the number of brown-like adipocytes (multilocular lipid droplets, uncoupling protein-1 immunoreactivity) at both warm and cold ambient temperatures. WAT and BAT have sensory innervation, the latter important for acute BAT cold-induced temperature increases, therefore suggesting the possible importance of sensory neural feedback from brite/beige cells for heat production. Only one report shows browned WAT capable of producing heat in vivo. Collectively, increases in WAT sympathetic drive and the phenotype of these stimulated adipocytes seems critical for the production of new and/or transdifferentiation of white to brite/beige adipocytes. Selective harnessing of WAT SNS drive to produce browning or selective browning independent of the SNS to counter increases in adiposity by increasing expenditure appears to be extremely challenging. PMID:27152173

  19. Neural control of white, beige and brown adipocytes

    PubMed Central

    Bartness, T J; Ryu, V

    2015-01-01

    Reports of brown-like adipocytes in traditionally white adipose tissue (WAT) depots occurred ~30 years ago, but interest in white adipocyte ‘browning' only has gained attention more recently. We integrate some of what is known about the sympathetic nervous system (SNS) innervation of WAT and brown adipose tissue (BAT) with the few studies focusing on the sympathetic innervation of the so-called ‘brite' or ‘beige' adipocytes that appear when WAT sympathetic drive increases (for example, cold exposure and food deprivation). Only one brain site, the dorsomedial hypothalamic nucleus (DMH), selectively browns some (inguinal WAT (IWAT) and dorsomedial subcutaneous WAT), but not all WAT depots and only when DMH neuropeptide Y gene expression is knocked down, a browning effect is mediated by WAT SNS innervation. Other studies show that WAT sympathetic fiber density is correlated with the number of brown-like adipocytes (multilocular lipid droplets, uncoupling protein-1 immunoreactivity) at both warm and cold ambient temperatures. WAT and BAT have sensory innervation, the latter important for acute BAT cold-induced temperature increases, therefore suggesting the possible importance of sensory neural feedback from brite/beige cells for heat production. Only one report shows browned WAT capable of producing heat in vivo. Collectively, increases in WAT sympathetic drive and the phenotype of these stimulated adipocytes seems critical for the production of new and/or transdifferentiation of white to brite/beige adipocytes. Selective harnessing of WAT SNS drive to produce browning or selective browning independent of the SNS to counter increases in adiposity by increasing expenditure appears to be extremely challenging. PMID:27152173

  20. The neural basis of inhibition in cognitive control.

    PubMed

    Aron, Adam R

    2007-06-01

    The concept of "inhibition" is widely used in synaptic, circuit, and systems neuroscience, where it has a clear meaning because it is clearly observable. The concept is also ubiquitous in psychology. One common use is to connote an active/willed process underlying cognitive control. Many authors claim that subjects execute cognitive control over unwanted stimuli, task sets, responses, memories, and emotions by inhibiting them, and that frontal lobe damage induces distractibility, impulsivity, and perseveration because of damage to an inhibitory mechanism. However, with the exception of the motor domain, the notion of an active inhibitory process underlying cognitive control has been heavily challenged. Alternative explanations have been provided that explain cognitive control without recourse to inhibition as concept, mechanism, or theory. This article examines the role that neuroscience can play when examining whether the psychological concept of active inhibition can be meaningfully applied in cognitive control research.

  1. [Therapeutic Effects of Multipotent Mesenchymal Stromal Cells after Irradiation].

    PubMed

    Kalmykova, N V; Alexandrova, S A

    2016-01-01

    Multipotent mesenchymal stromal cells (MSC) are now considered to be a perspective multifunctional treatment option for radiation side effects. At present.a great number of sufficient evidence has been collected in favor of therapeutic effects of MSCs in acute radiation reactions. It has been shown that MSC-based products injected locally or systemically have therapeutic effects on irradiated organs and tissues. This review presents summarized experimental and clinical data about protective and regenerative effects of MSCs on different radiation-injured organs and tissues; the main probable therapeutic mechanisms of their action are also discussed. PMID:27534063

  2. Neural adaptive control of nonlinear multivariable systems with application to a class of inverted pendulums.

    PubMed

    He, Shouling

    2002-10-01

    In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots.

  3. Application of neural networks with orthogonal activation functions in control of dynamical systems

    NASA Astrophysics Data System (ADS)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  4. The potential of mouse skin-derived precursors to differentiate into mesenchymal and neural lineages and their application to osteogenic induction in vivo.

    PubMed

    Kang, Hyun Ki; Min, Seung-Ki; Jung, Sung Youn; Jung, Kyoungsuk; Jang, Da Hyun; Kim, O Bok; Chun, Gae-Sig; Lee, Zang Hee; Min, Byung-Moo

    2011-12-01

    Although previous studies indicate that skin-derived precursors (SKPs) are multipotent dermal precursors that share similarities with neural crest stem cells (NCSCs), a shared ability for multilineage differentiation toward neural crest lineages between SKPs and NCSCs has not been fully demonstrated. Here, we report the derivation of SKPs from adult mouse skin and their directed multilineage differentiation toward neural crest lineages. Under controlled in vitro conditions, mouse SKPs were propagated and directed toward peripheral nervous system lineages such as peripheral neurons and Schwann cells, and mesenchymal lineages, such as osteogenic, chondrogenic, adipogenic, and smooth muscle cells. To ask if SKPs could generate these same lineages in vivo, a mixture of SKP-derived mesenchymal stem cells and hydroxyapatite/tricalcium phosphate was transplanted into the rat calvarial defects. Over the ensuing 4 weeks, we observed formation of osteogenic structure in the calvarial defect without any evidence of teratomas. These findings demonstrate the multipotency of adult mouse SKPs to differentiate into neural crest lineages. In addition, SKP-derived mesenchymal stem cells represent an accessible, potentially autologous source of precursor cells for tissue-engineered bone repair. PMID:21879252

  5. Nonlinear recurrent neural network predictive control for energy distribution of a fuel cell powered robot.

    PubMed

    Chen, Qihong; Long, Rong; Quan, Shuhai; Zhang, Liyan

    2014-01-01

    This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell.

  6. Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot

    PubMed Central

    Chen, Qihong; Long, Rong; Quan, Shuhai

    2014-01-01

    This paper presents a neural network predictive control strategy to optimize power distribution for a fuel cell/ultracapacitor hybrid power system of a robot. We model the nonlinear power system by employing time variant auto-regressive moving average with exogenous (ARMAX), and using recurrent neural network to represent the complicated coefficients of the ARMAX model. Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. The proposed algorithm significantly simplifies implementation of the controller and can handle multiple constraints, such as limiting substantial fluctuation of fuel cell current. Experiment and simulation results demonstrate that the control strategy can optimally split power between the fuel cell and ultracapacitor, limit the change rate of the fuel cell current, and so as to extend the lifetime of the fuel cell. PMID:24707206

  7. Evolution of an artificial neural network based autonomous land vehicle controller.

    PubMed

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  8. Two neural network algorithms for designing optimal terminal controllers with open final time

    NASA Technical Reports Server (NTRS)

    Plumer, Edward S.

    1992-01-01

    Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

  9. Robust adaptive neural control for a class of uncertain MIMO nonlinear systems

    NASA Astrophysics Data System (ADS)

    Wang, Chenliang; Lin, Yan

    2015-08-01

    In this paper, a novel robust adaptive neural control scheme is proposed for a class of uncertain multi-input multi-output nonlinear systems. The proposed scheme has the following main features: (1) a kind of Hurwitz condition is introduced to handle the state-dependent control gain matrix and some assumptions in existing schemes are relaxed; (2) by introducing a novel matrix normalisation technique, it is shown that all bound restrictions imposed on the control gain matrix in existing schemes can be removed; (3) the singularity problem is avoided without any extra effort, which makes the control law quite simple. Besides, with the aid of the minimal learning parameter technique, only one parameter needs to be updated online regardless of the system input-output dimension and the number of neural network nodes. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

  10. Modeling and control of an electric arc furnace using a feedforward artificial neural network

    NASA Astrophysics Data System (ADS)

    King, P. E.; Nyman, M. D.

    1996-08-01

    Previous studies have shown that the electric arc furnace is chaotic in nature and hence standard control techniques are not effective. However, human (heuristic) control is used every day on electric arc furnaces. A furnace operator assesses the performance of the furnace and makes judgments based on past experience and intuition. In order to improve the effectiveness of this control, a qualitative understanding of the operating conditions of the furnace is required. Artificial neural networks are capable of learning the system dynamics of the electric arc furnace. This article describes a feedforward neural network trained to model arc furnace electrical wave forms taken from an experimental arc furnace. The output of this model is then used in estimating the future state of the furnace for control purposes.

  11. Artificial neural networks and approximate reasoning for intelligent control in space

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

  12. Neural signature of reward-modulated unconscious inhibitory control.

    PubMed

    Diao, Liuting; Qi, Senqing; Xu, Mengsi; Li, Zhiai; Ding, Cody; Chen, Antao; Zheng, Yan; Yang, Dong

    2016-09-01

    Consciously initiated cognitive control is generally determined by motivational incentives (e.g., monetary reward). Recent studies have revealed that human cognitive control processes can nevertheless operate without awareness. However, whether monetary reward can impinge on unconscious cognitive control remains unclear. To clarify this issue, a task consisting of several runs was designed to combine a modified version of the reward-priming paradigm with an unconscious version of the Go/No-Go task. At the beginning of each run, participants were exposed to a high- or low-value coin, followed by the modified Go/No-Go task. Participants could earn the coin only if they responded correctly to each trial of the run. Event-related potential (ERP) results indicated that high-value rewards (vs. low-value rewards) induced a greater centro-parietal P3 component associated with conscious and unconscious inhibitory control. Moreover, the P3 amplitude correlated positively with the magnitude of reaction time slowing reflecting the intensity of activation of unconscious inhibitory control in the brain. These findings suggest that high-value reward may facilitate human higher-order inhibitory processes that are independent of conscious awareness, which provides insights into the brain processes that underpin motivational modulation of cognitive control. PMID:27346057

  13. Estimating Neural Background Input with Controlled and Fast Perturbations: A Bandwidth Comparison between Inhibitory Opsins and Neural Circuits.

    PubMed

    Eriksson, David

    2016-01-01

    To test the importance of a certain cell type or brain area it is common to make a "lack of function" experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 ms before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled "lack of function" experiments, but, it also suggests that such a "lack of function" experiment can be used to measure the context of local neural computations. PMID:27574506

  14. Controlling basins of attraction in a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Bell, Benjamin; Eilbert, James L.

    1988-01-01

    The size of the basins of attraction around fixed points in recurrent neural nets (NNs) can be modified by a training process. Controlling these attractive regions by presenting training data with various amount of noise added to the prototype signal vectors is discussed. Application of this technique to signal processing results in a classification system whose sensitivity can be controlled. This new technique is applied to the classification of temporal sequences in telemetry data.

  15. Neural control of lower urinary tract and targets for pharmacological therapy.

    PubMed

    Bortolini, Maria Augusta T; Bilhar, Andreisa P M; Castro, Rodrigo A

    2014-11-01

    Studies on the physiology and pharmacology of the lower urinary tract have brought new information and concepts about the complex neural control of micturition. There are many mechanisms, some proven and others not yet completely understood, in which pharmacological agents may act facilitating the filling, storage, and emptying of the bladder. This review describes the peripheral innervation and the main pathways involved in lower urinary tract control. It also presents potential targets for the treatment of voiding dysfunctions. PMID:25001574

  16. Neural-network predictive control for nonlinear dynamic systems with time-delay.

    PubMed

    Huang, Jin-Quan; Lewis, F L

    2003-01-01

    A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

  17. A neural command circuit for grooming movement control.

    PubMed

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-09-07

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila.

  18. Neural emotion regulation circuitry underlying anxiolytic effects of perceived control over pain.

    PubMed

    Salomons, Tim V; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J

    2015-02-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within-subject designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group ("controllable") was led to believe they had behavioral control over the pain stimuli, whereas another ("uncontrollable") believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala, and increased activation in nucleus accumbens. In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial pFC. The location of pFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between pFC and both amygdala and nucleus accumbens are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion.

  19. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    PubMed

    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. PMID:25608292

  20. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia*

    PubMed Central

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

    2010-01-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. PMID:19015583

  1. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    PubMed

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods. PMID:17668664

  2. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    PubMed

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

  3. Lactic Acid Bacteria Convert Human Fibroblasts to Multipotent Cells

    PubMed Central

    Ohta, Kunimasa; Kawano, Rie; Ito, Naofumi

    2012-01-01

    The human gastrointestinal tract is colonized by a vast community of symbionts and commensals. Lactic acid bacteria (LAB) form a group of related, low-GC-content, gram-positive bacteria that are considered to offer a number of probiotic benefits to general health. While the role of LAB in gastrointestinal microecology has been the subject of extensive study, little is known about how commensal prokaryotic organisms directly influence eukaryotic cells. Here, we demonstrate the generation of multipotential cells from adult human dermal fibroblast cells by incorporating LAB. LAB-incorporated cell clusters are similar to embryoid bodies derived from embryonic stem cells and can differentiate into endodermal, mesodermal, and ectodermal cells in vivo and in vitro. LAB-incorporated cell clusters express a set of genes associated with multipotency, and microarray analysis indicates a remarkable increase of NANOG, a multipotency marker, and a notable decrease in HOX gene expression in LAB-incorporated cells. During the cell culture, the LAB-incorporated cell clusters stop cell division and start to express early senescence markers without cell death. Thus, LAB-incorporated cell clusters have potentially wide-ranging implications for cell generation, reprogramming, and cell-based therapy. PMID:23300571

  4. Characteristics and multipotency of equine dedifferentiated fat cells

    PubMed Central

    MURATA, Daiki; YAMASAKI, Atsushi; MATSUZAKI, Shouta; SUNAGA, Takafumi; FUJIKI, Makoto; TOKUNAGA, Satoshi; MISUMI, Kazuhiro

    2016-01-01

    ABSTRACT Dedifferentiated fat (DFAT) cells have been shown to be multipotent, similar to mesenchymal stem cells (MSCs). In this study, we aimed to establish and characterize equine DFAT cells. Equine adipocytes were ceiling cultured, and then dedifferentiated into DFAT cells by the seventh day of culture. The number of DFAT cells was increased to over 10 million by the fourth passage. Flow cytometry of DFAT cells showed that the cells were strongly positive for CD44, CD90, and major histocompatibility complex (MHC) class I; moderately positive for CD11a/18, CD105, and MHC class II; and negative for CD34 and CD45. Moreover, DFAT cells were positive for the expression of sex determining region Y-box 2 as a marker of multipotency. Finally, we found that DFAT cells could differentiate into osteogenic, chondrogenic, and adipogenic lineages under specific nutrient conditions. Thus, DFAT cells could have clinical applications in tissue regeneration, similar to MSCs derived from adipose tissue. PMID:27330399

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

    ERIC Educational Resources Information Center

    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.…

  6. Identification and speed control of ultrasonic motors based on neural networks

    NASA Astrophysics Data System (ADS)

    Xu, X.; Liang, Y. C.; Lee, H. P.; Lin, W. Z.; Lim, S. P.; Lee, K. H.; Shi, X. H.

    2003-01-01

    An ultrasonic motor (USM) is a newly developed motor that has many excellent performances, useful features and extensive applications. The operational characteristics of the USM are affected by many factors. Strongly nonlinear characteristics could be caused by the increase of temperature, the changes of load, driving frequency and voltage and many other factors. Therefore, it is difficult to perform effective control on USMs using traditional control methods based on mathematical models of systems. Recently, artificial intelligent methods based on neural networks have become the main approaches to perform USM control. However, the existing neural-network-based methods for USM control have some shortcomings, such as complex network structures, slower convergent speeds and lower convergent precision, as well as no theoretical guarantee on the convergence of control. Furthermore, it is difficult to obtain accurate control input for the USM by using a speed controller with a single control variable. In this paper, a bimodal controller is designed where both the driving frequency and amplitude of the applied voltage are used as control inputs. A novel input-output recurrent neural network (IORNN) identifier is constructed to dynamically identify the input-output relation of the ultrasonic motors. To guarantee convergence and for faster learning, the adaptive learning rates are derived using discrete-type Lyapunov stability analysis. Numerical results show that the proposed IORNN identifier can approximate the nonlinear input-output mapping of ultrasonic motors quite well. Compared with the existing method, the control precision can be increased by about three times and the convergence time can be decreased by about two times when the proposed method is employed. Good effectiveness of the proposed control scheme is also obtained for various reference speeds.

  7. Temporal dynamics and potential neural sources of goal conduciveness, control, and power appraisal.

    PubMed

    Gentsch, Kornelia; Grandjean, Didier; Scherer, Klaus R

    2015-12-01

    A major emotion theory, the Component Process Model, predicts that emotion-antecedent appraisal proceeds sequentially (e.g., goal conduciveness>control>power appraisal). In a gambling task, feedback manipulated information about goal conduciveness (outcome: win, loss), control (perceived high and low control), and power appraisals (choice options to change the outcome). Using mean amplitudes of event-related potentials, we examine the sequential prediction of these appraisal criteria. Additionally, we apply source localization analysis to estimate the neural sources of the evoked components of interest. Early ERPs (230-300 ms) show main effects of goal conduciveness and power but no interaction effects suggesting goal obstructiveness assessment of task-relevant feedback information. Late ERPs (350-600 ms) reveal main effects of all appraisals and interaction effects representing the integration of all appraisal information. Source localization analysis suggests distinct neural sources for these appraisal criteria.

  8. Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

    PubMed Central

    Vlachos, Ioannis; Deniz, Taşkin; Aertsen, Ad; Kumar, Arvind

    2016-01-01

    There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system. PMID:26829673

  9. Circadian rhythm of temperature preference and its neural control in Drosophila

    PubMed Central

    Kaneko, Haruna; Head, Lauren M.; Ling, Jinli; Tang, Xin; Liu, Yilin; Hardin, Paul E.; Emery, Patrick; Hamada, Fumika N.

    2012-01-01

    A daily body temperature rhythm (BTR) is critical for the maintenance of homeostasis in mammals. While mammals use internal energy to regulate body temperature, ectotherms typically regulate body temperature behaviorally [1]. Some ectotherms maintain homeostasis via a daily temperature preference rhythm (TPR) [2], but the underlying mechanisms are largely unknown. Here, we show that Drosophila exhibit a daily circadian clock dependent TPR that resembles mammalian BTR. Pacemaker neurons critical for locomotor activity are not necessary for TPR; instead, the dorsal neuron 2s (DN2s), whose function was previously unknown, is sufficient. This indicates that TPR, like BTR, is controlled independently from locomotor activity. Therefore, the mechanisms controlling temperature fluctuations in fly TPR and mammalian BTR may share parallel features. Taken together, our results reveal the existence of a novel DN2- based circadian neural circuit that specifically regulates TPR; thus, understanding the mechanisms of TPR will shed new light on the function and neural control of circadian rhythms. PMID:22981774

  10. Adaptive neural control for an uncertain robotic manipulator with joint space constraints

    NASA Astrophysics Data System (ADS)

    Tang, Zhong-Liang; Ge, Shuzhi Sam; Tee, Keng Peng; He, Wei

    2016-07-01

    In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.

  11. Neural Emotion Regulation Circuitry Underlying Anxiolytic Effects of Perceived Control Over Pain

    PubMed Central

    Salomons, Tim V.; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J.

    2014-01-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within subjects-designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group (“controllable”) was led to believe they had behavioral control over the pain stimuli while another (“uncontrollable”) believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala and increased activation in nucleus accumbens (NAcc). In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial prefrontal cortex (PFC). The location of PFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between PFC and both amygdala and NAcc are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion. PMID:25208742

  12. Identification and control of plasma vertical position using neural network in Damavand tokamak

    SciTech Connect

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-02-15

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  13. Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique.

    PubMed

    Min Wang; Xiaoping Liu; Peng Shi

    2011-12-01

    This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid "the explosion of complexity" in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.

  14. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, C.Y.

    1998-11-24

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

  15. Closed loop adaptive control of spectrum-producing step using neural networks

    DOEpatents

    Fu, Chi Yung

    1998-01-01

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

  16. Identification and control of plasma vertical position using neural network in Damavand tokamak

    NASA Astrophysics Data System (ADS)

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-02-01

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  17. A direct self-constructing neural controller design for a class of nonlinear systems.

    PubMed

    Han, Honggui; Zhou, Wendong; Qiao, Junfei; Feng, Gang

    2015-06-01

    This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method. PMID:25706896

  18. CMAC neural network architecture for control of an autonomous undersea vehicle

    NASA Astrophysics Data System (ADS)

    Comoglio, Rick F.; Pandya, Abhijit S.

    1992-09-01

    The design of an autonomous undersea vehicle (AUV) control system is a significant challenge in light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This paper describes a neural network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an autonomous underwater vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.

  19. Shared neural processes support semantic control and action understanding

    PubMed Central

    Davey, James; Rueschemeyer, Shirley-Ann; Costigan, Alison; Murphy, Nik; Krieger-Redwood, Katya; Hallam, Glyn; Jefferies, Elizabeth

    2015-01-01

    Executive–semantic control and action understanding appear to recruit overlapping brain regions but existing evidence from neuroimaging meta-analyses and neuropsychology lacks spatial precision; we therefore manipulated difficulty and feature type (visual vs. action) in a single fMRI study. Harder judgements recruited an executive–semantic network encompassing medial and inferior frontal regions (including LIFG) and posterior temporal cortex (including pMTG). These regions partially overlapped with brain areas involved in action but not visual judgements. In LIFG, the peak responses to action and difficulty were spatially identical across participants, while these responses were overlapping yet spatially distinct in posterior temporal cortex. We propose that the co-activation of LIFG and pMTG allows the flexible retrieval of semantic information, appropriate to the current context; this might be necessary both for semantic control and understanding actions. Feature selection in difficult trials also recruited ventral occipital–temporal areas, not implicated in action understanding. PMID:25658631

  20. Insulin–InsR signaling drives multipotent progenitor differentiation toward lymphoid lineages

    PubMed Central

    Xia, Pengyan; Wang, Shuo; Du, Ying; Huang, Guanling; Satoh, Takashi; Akira, Shizuo

    2015-01-01

    The lineage commitment of HSCs generates balanced myeloid and lymphoid populations in hematopoiesis. However, the underlying mechanisms that control this process remain largely unknown. Here, we show that insulin–insulin receptor (InsR) signaling is required for lineage commitment of multipotent progenitors (MPPs). Deletion of Insr in murine bone marrow causes skewed differentiation of MPPs to myeloid cells. mTOR acts as a downstream effector that modulates MPP differentiation. mTOR activates Stat3 by phosphorylation at serine 727 under insulin stimulation, which binds to the promoter of Ikaros, leading to its transcription priming. Our findings reveal that the insulin–InsR signaling drives MPP differentiation into lymphoid lineages in early lymphopoiesis, which is essential for maintaining a balanced immune system for an individual organism. PMID:26573296

  1. Acetylcholine mediates behavioral and neural post-error control.

    PubMed

    Danielmeier, Claudia; Allen, Elena A; Jocham, Gerhard; Onur, Oezguer A; Eichele, Tom; Ullsperger, Markus

    2015-06-01

    Humans often commit errors when they are distracted by irrelevant information and no longer focus on what is relevant to the task at hand. Adjustments following errors are essential for optimizing goal achievement. The posterior medial frontal cortex (pMFC), a key area for monitoring errors, has been shown to trigger such post-error adjustments by modulating activity in visual cortical areas. However, the mechanisms by which pMFC controls sensory cortices are unknown. We provide evidence for a mechanism based on pMFC-induced recruitment of cholinergic projections to task-relevant sensory areas. Using fMRI in healthy volunteers, we found that error-related pMFC activity predicted subsequent adjustments in task-relevant visual brain areas. In particular, following an error, activity increased in those visual cortical areas involved in processing task-relevant stimulus features, whereas activity decreased in areas representing irrelevant, distracting features. Following treatment with the muscarinic acetylcholine receptor antagonist biperiden, activity in visual areas was no longer under control of error-related pMFC activity. This was paralleled by abolished post-error behavioral adjustments under biperiden. Our results reveal a prominent role of acetylcholine in cognitive control that has not been recognized thus far. Regaining optimal performance after errors critically depends on top-down control of perception driven by the pMFC and mediated by acetylcholine. This may explain the lack of adaptivity in conditions with reduced availability of cortical acetylcholine, such as Alzheimer's disease.

  2. A neural command circuit for grooming movement control

    PubMed Central

    Hampel, Stefanie; Franconville, Romain; Simpson, Julie H; Seeds, Andrew M

    2015-01-01

    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. Here we use anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, we find differences in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, we infer the possibility of a common circuit organization for movement control that can be dissected in Drosophila. DOI: http://dx.doi.org/10.7554/eLife.08758.001 PMID:26344548

  3. Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs.

    PubMed

    Lin, F J; Wai, R J; Hong, C M

    2001-01-01

    A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

  4. Robust fuzzy neural network sliding mode control scheme for IPMSM drives

    NASA Astrophysics Data System (ADS)

    Leu, V. Q.; Mwasilu, F.; Choi, H. H.; Lee, J.; Jung, J. W.

    2014-07-01

    This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.

  5. Calcium antagonists and neural control of circulation in essential hypertension.

    PubMed

    Mancia, G; Parati, G; Grassi, G; Pomidossi, G; Giannattasio, C; Casadei, R; Groppelli, A; Saino, A; Gregorini, L; Perondi, R

    1987-12-01

    Data from animals and from man suggest that calcium antagonists interfere with alpha-adrenergic receptors and that this mechanism may be responsible for some of the vasodilation induced by these drugs. However, alpha-adrenergic receptors play a primary role in baroreceptor regulation of the cardiovascular system and blood pressure homeostasis, which might therefore be adversely affected by calcium antagonist treatment. We addressed this question in 14 essential hypertensives studied before treatment, 1 h after 20 mg oral nitrendipine and 5-7 days after daily administration of 20 mg oral nitrendipine. Blood pressure was measured by an intra-arterial catheter, heart rate by an electrocardiogram, cardiac output by thermodilution and forearm blood flow by venous occlusion plethysmography. Total peripheral and forearm vascular resistances were calculated by dividing mean blood pressure by blood flow values. Plasma norepinephrine was also measured (high performance liquid chromatography) in blood taken from the right atrium. Compared with the pretreatment values, acute nitrendipine administration caused a fall in resting blood pressure, an increase in the resting heart rate and cardiac output, and a fall in resting peripheral and forearm vascular resistance. The resting hypotension and vasodilation were also evident during the prolonged nitrendipine administration, which was, however, accompanied by much less resting cardiac stimulation than that observed in the acute condition. Baroreceptor control of the heart rate (vasoactive drug method) was similar before and after acute and prolonged nitrendipine treatment. This was also the case for carotid baroreceptor control of blood pressure (neck chamber technique) and for control of forearm vascular resistance as exerted by receptors in the cardiopulmonary region (lower-body negative-pressure and passive leg-raising techniques).(ABSTRACT TRUNCATED AT 250 WORDS)

  6. Age-related modifications in neural cardiovascular control.

    PubMed

    Ferrari, A U

    1992-09-01

    Integrated cardiovascular responses to a range of different stimuli, as well as the overall, spontaneously occurring variability in blood pressure and heart rate, undergo complex changes with aging. A general trend is that homeostatic control mechanisms lose part of their ability to modulate heart rate and to buffer the concomitant blood pressure variations; the two phenomena are possibly linked by a cause-effect relationship. A detailed analysis of the age-related changes in the major reflex systems reveals a clear-cut impairment in arterial baroreceptor control of the heart rate, but much less pronounced changes in its control of blood pressure, on the other hand, both the hemodynamic and humoral components of the cardiopulmonary reflex appear to be markedly attenuated. The experimental evidence of the mechanisms underlying these changes is still largely incomplete, and it appears that the gaps will have to be filled by a systematic, detailed analysis, i.e., that no generalizations or extrapolations will be possible. Indeed, the data available so far indicate that the age-related alterations are highly non-uniform, some functions undergoing a definite impairment but others being much better preserved and some being even enhanced; thus aging is by no means associated with a generalized decline in cardiovascular functions and should instead be viewed as a complex, highly selective process. These peculiar biological features of the aging phenomena merit further investigation in both the cardiovascular and the other organ systems, in order to verify the possibility that currently unrecognized homeostatic potentials in the elderly subject may be exploited to advance his/her clinical management in health and disease.

  7. Remote control of respiratory neural network by spinal locomotor generators.

    PubMed

    Le Gal, Jean-Patrick; Juvin, Laurent; Cardoit, Laura; Thoby-Brisson, Muriel; Morin, Didier

    2014-01-01

    During exercise and locomotion, breathing rate rapidly increases to meet the suddenly enhanced oxygen demand. The extent to which direct central interactions between the spinal networks controlling locomotion and the brainstem networks controlling breathing are involved in this rhythm modulation remains unknown. Here, we show that in isolated neonatal rat brainstem-spinal cord preparations, the increase in respiratory rate observed during fictive locomotion is associated with an increase in the excitability of pre-inspiratory neurons of the parafacial respiratory group (pFRG/Pre-I). In addition, this locomotion-induced respiratory rhythm modulation is prevented both by bilateral lesion of the pFRG region and by blockade of neurokinin 1 receptors in the brainstem. Thus, our results assign pFRG/Pre-I neurons a new role as elements of a previously undescribed pathway involved in the functional interaction between respiratory and locomotor networks, an interaction that also involves a substance P-dependent modulating mechanism requiring the activation of neurokinin 1 receptors. This neurogenic mechanism may take an active part in the increased respiratory rhythmicity produced at the onset and during episodes of locomotion in mammals.

  8. Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms.

    PubMed

    Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan

    2016-04-01

    Two types of coupled neural networks with reaction-diffusion terms are considered in this paper. In the first one, the nodes are coupled through their states. In the second one, the nodes are coupled through the spatial diffusion terms. For the former, utilizing Lyapunov functional method and pinning control technique, we obtain some sufficient conditions to guarantee that network can realize synchronization. In addition, considering that the theoretical coupling strength required for synchronization may be much larger than the needed value, we propose an adaptive strategy to adjust the coupling strength for achieving a suitable value. For the latter, we establish a criterion for synchronization using the designed pinning controllers. It is found that the coupled reaction-diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled reaction-diffusion neural networks with spatial diffusion coupling. Moreover, a general criterion for ensuring network synchronization is derived by pinning a small fraction of nodes with adaptive feedback controllers. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

  9. Neural Autoantibody Evaluation in Functional Gastrointestinal Disorders: A Population-Based Case–Control Study

    PubMed Central

    Pittock, Sean J.; Lennon, Vanda A.; Dege, Carissa L.; Talley, Nicholas J.; Richard Locke, G.

    2011-01-01

    Background Our goal is to investigate the serum profile of neural autoantibodies in community-based patients with irritable bowel syndrome (IBS) or functional dyspepsia. The pathogenesis of functional gastrointestinal (GI) disorders, including IBS and dyspepsia, are unknown. Theories range from purely psychological to autoimmune alterations in GI tract neuromuscular function. Methods The study subjects, based in Olmsted County, MN, reported symptoms of functional dyspepsia or IBS (n = 69), or were asymptomatic controls (n = 64). Their coded sera were screened for antibodies targeting neuronal, glial, and muscle autoantigens. Results The prevalence of neural autoantibodies with functional GI disorders did not differ significantly from controls (17% vs. 13%; P = 0.43). In no case was a neuronal or glial nuclear autoantibody or enteric neuronal autoantibody identified. Neuronal cation channel antibodies were identified in 9% of cases (voltage-gated potassium channel [VGKC] in one dyspepsia case and one IBS case, ganglionic acetylcholine receptor [AChR] in four IBS cases) and in 6% of controls (ganglionic AChR in one, voltage-gated calcium channel [VGCC], N-type, in two and VGKC in one; P = 0.36). The frequency of glutamic acid decarboxylase-65 (GAD65) autoantibodies was similar in cases (10%) and controls (5%; P = 0.23). Conclusions Our data do not support neural autoimmunity as the basis for most IBS or functional dyspepsia cases. PMID:21181442

  10. Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation

    NASA Astrophysics Data System (ADS)

    Gao, Shigen; Dong, Hairong; Lyu, Shihang; Ning, Bin

    2016-07-01

    This paper studies decentralised neural adaptive control of a class of interconnected nonlinear systems, each subsystem is in the presence of input saturation and external disturbance and has independent system order. Using a novel truncated adaptation design, dynamic surface control technique and minimal-learning-parameters algorithm, the proposed method circumvents the problems of 'explosion of complexity' and 'dimension curse' that exist in the traditional backstepping design. Comparing to the methodology that neural weights are online updated in the controllers, only one scalar needs to be updated in the controllers of each subsystem when dealing with unknown systematic dynamics. Radial basis function neural networks (NNs) are used in the online approximation of unknown systematic dynamics. It is proved using Lyapunov stability theory that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The tracking errors of each subsystems, the amplitude of NN approximation residuals and external disturbances can be attenuated to arbitrarily small by tuning proper design parameters. Simulation results are given to demonstrate the effectiveness of the proposed method.

  11. Epileptogenic zone localization and seizure control in coupled neural mass models.

    PubMed

    Ma, Zhen; Zhou, Weidong; Zhang, Yanli; Geng, Shujuan

    2015-12-01

    Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses. PMID:26585963

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

    PubMed

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

    2015-12-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive control. The similarity between the networks underlying bilingual language control and general executive control supports the notion that the frequently reported bilingual advantage on executive tasks stems from the day-to-day demands of language control in the multilingual brain. We examined neural correlates of the management of simultaneity by correlating brain activity during interpretation with the duration of simultaneous speaking and hearing. This analysis showed significant modulation of the putamen by the duration of simultaneity. Our findings suggest that, during SI, the caudate nucleus is implicated in the overarching selection and control of the lexico-semantic system, while the putamen is implicated in ongoing control of language output. These findings provide the first clear dissociation of specific dorsal striatum structures in polyglot language control, roles that are consistent with previously described involvement of these regions in nonlinguistic executive control. PMID:25037924

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

    PubMed

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

    2015-12-01

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

  14. Dynamic recurrent neural networks for stable adaptive control of wing rock motion

    NASA Astrophysics Data System (ADS)

    Kooi, Steven Boon-Lam

    Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to

  15. Estimating Neural Background Input with Controlled and Fast Perturbations: A Bandwidth Comparison between Inhibitory Opsins and Neural Circuits

    PubMed Central

    Eriksson, David

    2016-01-01

    To test the importance of a certain cell type or brain area it is common to make a “lack of function” experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 ms before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled “lack of function” experiments, but, it also suggests that such a “lack of function” experiment can be used to measure the context of local neural computations. PMID:27574506

  16. Neural network evaluation of tokamak current profiles for real time control (abstract)

    SciTech Connect

    Wroblewski, D.

    1997-01-01

    Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q{sub 0}, minimum value of q, q{sub min}, and the location of q{sub min}. Very good performance of the trained neural network both for simulated test data and for experimental data is demonstrated. {copyright} {ital 1997 American Institute of Physics.}

  17. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

    PubMed

    Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

    2012-05-30

    The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.

  18. Stereotypical cell division orientation controls neural rod midline formation in zebrafish.

    PubMed

    Quesada-Hernández, Elena; Caneparo, Luca; Schneider, Sylvia; Winkler, Sylke; Liebling, Michael; Fraser, Scott E; Heisenberg, Carl-Philipp

    2010-11-01

    The development of multicellular organisms is dependent on the tight coordination between tissue growth and morphogenesis. The stereotypical orientation of cell divisions has been proposed to be a fundamental mechanism by which proliferating and growing tissues take shape. However, the actual contribution of stereotypical division orientation (SDO) to tissue morphogenesis is unclear. In zebrafish, cell divisions with stereotypical orientation have been implicated in both body-axis elongation and neural rod formation, although there is little direct evidence for a critical function of SDO in either of these processes. Here we show that SDO is required for formation of the neural rod midline during neurulation but dispensable for elongation of the body axis during gastrulation. Our data indicate that SDO during both gastrulation and neurulation is dependent on the noncanonical Wnt receptor Frizzled 7 (Fz7) and that interfering with cell division orientation leads to severe defects in neural rod midline formation but not body-axis elongation. These findings suggest a novel function for Fz7-controlled cell division orientation in neural rod midline formation during neurulation.

  19. LOXL2 Oxidizes Methylated TAF10 and Controls TFIID-Dependent Genes during Neural Progenitor Differentiation.

    PubMed

    Iturbide, Ane; Pascual-Reguant, Laura; Fargas, Laura; Cebrià, Joan Pau; Alsina, Berta; García de Herreros, Antonio; Peiró, Sandra

    2015-06-01

    Protein function is often regulated and controlled by posttranslational modifications, such as oxidation. Although oxidation has been mainly considered to be uncontrolled and nonenzymatic, many enzymatic oxidations occur on enzyme-selected lysine residues; for instance, LOXL2 oxidizes lysines by converting the ε-amino groups into aldehyde groups. Using an unbiased proteomic approach, we have identified methylated TAF10, a member of the TFIID complex, as a LOXL2 substrate. LOXL2 oxidation of TAF10 induces its release from its promoters, leading to a block in TFIID-dependent gene transcription. In embryonic stem cells, this results in the inactivation of the pluripotency genes and loss of the pluripotent capacity. During zebrafish development, the absence of LOXL2 resulted in the aberrant overexpression of the neural progenitor gene Sox2 and impaired neural differentiation. Thus, lysine oxidation of the transcription factor TAF10 is a controlled protein modification and demonstrates a role for protein oxidation in regulating pluripotency genes.

  20. Mutual information and self-control of a fully-connected low-activity neural network

    NASA Astrophysics Data System (ADS)

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  1. A neural network for controlling the configuration of frame structure with elastic members

    NASA Technical Reports Server (NTRS)

    Tsutsumi, Kazuyoshi

    1989-01-01

    A neural network for controlling the configuration of frame structure with elastic members is proposed. In the present network, the structure is modeled not by using the relative angles of the members but by using the distances between the joint locations alone. The relationship between the environment and the joints is also defined by their mutual distances. The analog neural network attains the reaching motion of the manipulator as a minimization problem of the energy constructed by the distances between the joints, the target, and the obstacles. The network can generate not only the final but also the transient configurations and the trajectory. This framework with flexibility and parallelism is very suitable for controlling the Space Telerobotic systems with many degrees of freedom.

  2. Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint.

    PubMed

    He, Wei; Dong, Yiting; Sun, Changyin

    2015-09-01

    In this paper, we aim to solve the control problem of nonlinear affine systems, under the condition of the input deadzone and output constraint with the external unknown disturbance. To eliminate the effects of the input deadzone, a Radial Basis Function Neural Network (RBFNN) is introduced to compensate for the negative impact of input deadzone. Meanwhile, we design a barrier Lyapunov function to ensure that the output parameters are restricted. In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods. The stability of the closed-loop system is proven via the Lyapunov method and the performance of the expected effects is verified in simulation.

  3. Isolation, culture and analysis of adult subependymal neural stem cells.

    PubMed

    Belenguer, Germán; Domingo-Muelas, Ana; Ferrón, Sacri R; Morante-Redolat, José Manuel; Fariñas, Isabel

    2016-01-01

    Individual cells dissected from the subependymal neurogenic niche of the adult mouse brain proliferate in medium containing basic fibroblast growth factor (bFGF) and/or epidermal growth factor (EGF) as mitogens, to produce multipotent clonal aggregates called neurospheres. These cultures constitute a powerful tool for the study of neural stem cells (NSCs) provided that they allow the analysis of their features and potential capacity in a controlled environment that can be modulated and monitored more accurately than in vivo. Clonogenic and population analyses under mitogen addition or withdrawal allow the quantification of the self-renewing and multilineage potency of these cells and the identification of the mechanisms involved in these properties. Here, we describe a set of procedures developed and/or modified by our group including several experimental options that can be used either independently or in combination for the ex vivo assessment of cell properties of NSCs obtained from the adult subependymal niche. PMID:27016251

  4. Integrated Neural and Endocrine Control of Gastrointestinal Function.

    PubMed

    Furness, John B

    2016-01-01

    The activity of the digestive system is dynamically regulated by external factors, including body nutritional and activity states, emotions and the contents of the digestive tube. The gut must adjust its activity to assimilate a hugely variable mixture that is ingested, particularly in an omnivore such as human for which a wide range of food choices exist. It must also guard against toxins and pathogens. These nutritive and non-nutritive components of the gut contents interact with the largest and most vulnerable surface in the body, the lining of the gastrointestinal tract. This requires a gut sensory system that can detect many classes of nutrients, non-nutrient components of food, physicochemical conditions, toxins, pathogens and symbionts (Furness et al., Nat Rev Gastroenterol Hepatol 10:729-740, 2013). The gut sensors are in turn coupled to effector systems that can respond to the sensory information. The responses are exerted through enteroendocrine cells (EEC), the enteric nervous system (ENS), the central nervous system (CNS) and the gut immune and tissue defence systems. It is apparent that the control of the digestive organs is an integrated function of these effectors. The peripheral components of the EEC, ENS and CNS triumvirate are extensive. EEC cells have traditionally been classified into about 12 types (disputed in this review), releasing about 20 hormones, together making the gut endocrine system the largest endocrine organ in the body. Likewise, in human the ENS contains about 500 million neurons, far more than the number of neurons in the remainder of the peripheral autonomic nervous system. Together gut hormones, the ENS and the CNS control or influence functions including satiety, mixing and propulsive activity, release of digestive enzymes, induction of nutrient transporters, fluid transport, local blood flow, gastric acid secretion, evacuation and immune responses. Gut content receptors, including taste, free fatty acid, peptide and

  5. Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

    PubMed

    Zhang, Fan; Liu, Ming; Harper, Stephen; Lee, Michael; Huang, He

    2014-01-01

    To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs. PMID:25079449

  6. Central neural control of the cardiovascular system: current perspectives.

    PubMed

    Dampney, Roger A L

    2016-09-01

    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. Such cardiovascular regulatory mechanisms do not operate in isolation but are closely coordinated with respiratory and other regulatory mechanisms to maintain homeostasis. The brain regulates the cardiovascular system by two general means: 1) feedforward regulation, often referred to as "central command," and 2) feedback or reflex regulation. In most situations (e.g., during exercise, defensive behavior, sleep, etc.), both of these general mechanisms contribute to overall cardiovascular homeostasis. The review first describes the mechanisms and central circuitry subserving the baroreceptor, chemoreceptor, and other reflexes that work together to regulate an appropriate level of blood pressure and blood oxygenation and then considers the brain mechanisms that defend the body against more complex environmental challenges, using dehydration and cold and heat stress as examples. The last section of the review considers the central mechanisms regulating cardiovascular function associated with different behaviors, with a specific focus on defensive behavior and exercise. PMID:27445275

  7. Neural Control of Blood Pressure in Chronic Intermittent Hypoxia

    PubMed Central

    Shell, Brent; Faulk, Katelynn; Cunningham, J. Thomas

    2016-01-01

    Sleep apnea (SA) is increasing in prevalence and is commonly comorbid with hypertension. Chronic intermittent hypoxia is used to model the arterial hypoxemia seen in SA, and through this paradigm, the mechanisms that underlie SA-induced hypertension are becoming clear. Cyclic hypoxic exposure during sleep chronically stimulates the carotid chemoreflexes, inducing sensory long-term facilitation, and drives sympathetic outflow from the hindbrain. The elevated sympathetic tone drives hypertension and renal sympathetic activity to the kidneys resulting in increased plasma renin activity and eventually angiotensin II (Ang II) peripherally. Upon waking, when respiration is normalized, the sympathetic activity does not diminish. This is partially because of adaptations leading to overactivation of the hindbrain regions controlling sympathetic outflow such as the nucleus tractus solitarius (NTS), and rostral ventrolateral medulla (RVLM). The sustained sympathetic activity is also due to enhanced synaptic signaling from the forebrain through the paraventricular nucleus (PVN). During the waking hours, when the chemoreceptors are not exposed to hypoxia, the forebrain circumventricular organs (CVOs) are stimulated by peripherally circulating Ang II from the elevated plasma renin activity. The CVOs and median preoptic nucleus chronically activate the PVN due to the Ang II signaling. All together, this leads to elevated nocturnal mean arterial pressure (MAP) as a response to hypoxemia, as well as inappropriately elevated diurnal MAP in response to maladaptations. PMID:26838032

  8. Central neural control of the cardiovascular system: current perspectives.

    PubMed

    Dampney, Roger A L

    2016-09-01

    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. Such cardiovascular regulatory mechanisms do not operate in isolation but are closely coordinated with respiratory and other regulatory mechanisms to maintain homeostasis. The brain regulates the cardiovascular system by two general means: 1) feedforward regulation, often referred to as "central command," and 2) feedback or reflex regulation. In most situations (e.g., during exercise, defensive behavior, sleep, etc.), both of these general mechanisms contribute to overall cardiovascular homeostasis. The review first describes the mechanisms and central circuitry subserving the baroreceptor, chemoreceptor, and other reflexes that work together to regulate an appropriate level of blood pressure and blood oxygenation and then considers the brain mechanisms that defend the body against more complex environmental challenges, using dehydration and cold and heat stress as examples. The last section of the review considers the central mechanisms regulating cardiovascular function associated with different behaviors, with a specific focus on defensive behavior and exercise.

  9. Genetic control of astrocyte function in neural circuits

    PubMed Central

    Jahn, Hannah M.; Scheller, Anja; Kirchhoff, Frank

    2015-01-01

    During the last two decades numerous genetic approaches affecting cell function in vivo have been developed. Current state-of-the-art technology permits the selective switching of gene function in distinct cell populations within the complex organization of a given tissue parenchyma. The tamoxifen-inducible Cre/loxP gene recombination and the doxycycline-dependent modulation of gene expression are probably the most popular genetic paradigms. Here, we will review applications of these two strategies while focusing on the interactions of astrocytes and neurons in the central nervous system (CNS) and their impact for the whole organism. Abolishing glial sensing of neuronal activity by selective deletion of glial transmitter receptors demonstrated the impact of astrocytes for higher cognitive functions such as learning and memory, or the more basic body control of muscle coordination. Interestingly, also interfering with glial output, i.e., the release of gliotransmitters can drastically change animal’s physiology like sleeping behavior. Furthermore, such genetic approaches have also been used to restore astrocyte function. In these studies two alternatives were employed to achieve proper genetic targeting of astrocytes: transgenes using the promoter of the human glial fibrillary acidic protein (GFAP) or homologous recombination into the glutamate-aspartate transporter (GLAST) locus. We will highlight their specific properties that could be relevant for their use. PMID:26347607

  10. Chaos control and synchronization, with input saturation, via recurrent neural networks.

    PubMed

    Sanchez, Edgar N; Ricalde, Luis J

    2003-01-01

    This paper deals with the adaptive tracking problem of non-linear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter. The new approach is illustrated by examples of complex dynamical systems: chaos control and synchronization. PMID:12850026

  11. Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

    NASA Astrophysics Data System (ADS)

    Wang, Z. P.; Zhou, T.; Mao, Y.; Chen, Q. J.

    2014-02-01

    In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.

  12. Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

    NASA Technical Reports Server (NTRS)

    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.

  13. Giant Panda (Ailuropoda melanoleuca) Buccal Mucosa Tissue as a Source of Multipotent Progenitor Cells.

    PubMed

    Prescott, Hilary M A; Manning, Craig; Gardner, Aaron; Ritchie, William A; Pizzi, Romain; Girling, Simon; Valentine, Iain; Wang, Chengdong; Jahoda, Colin A B

    2015-01-01

    Since the first mammal was cloned, the idea of using this technique to help endangered species has aroused considerable interest. However, several issues limit this possibility, including the relatively low success rate at every stage of the cloning process, and the dearth of usable tissues from these rare animals. iPS cells have been produced from cells from a number of rare mammalian species and this is the method of choice for strategies to improve cloning efficiency and create new gametes by directed differentiation. Nevertheless information about other stem cell/progenitor capabilities of cells from endangered species could prove important for future conservation approaches and adds to the knowledge base about cellular material that can be extremely limited. Multipotent progenitor cells, termed skin-derived precursor (SKP) cells, can be isolated directly from mammalian skin dermis, and human cheek tissue has also been shown to be a good source of SKP-like cells. Recently we showed that structures identical to SKPs termed m-SKPs could be obtained from monolayer/ two dimensional (2D) skin fibroblast cultures. Here we aimed to isolate m-SKPs from cultured cells of three endangered species; giant panda (Ailuropoda melanoleuca); red panda (Ailurus fulgens); and Asiatic lion (Panthera leo persica). m-SKP-like spheres were formed from the giant panda buccal mucosa fibroblasts; whereas dermal fibroblast (DF) cells cultured from abdominal skin of the other two species were unable to generate spheres. Under specific differentiation culture conditions giant panda spheres expressed neural, Schwann, adipogenic and osteogenic cell markers. Furthermore, these buccal mucosa derived spheres were shown to maintain expression of SKP markers: nestin, versican, fibronectin, and P75 and switch on expression of the stem cell marker ABCG2. These results demonstrate that giant panda cheek skin can be a useful source of m-SKP multipotent progenitors. At present lack of sample numbers

  14. Giant Panda (Ailuropoda melanoleuca) Buccal Mucosa Tissue as a Source of Multipotent Progenitor Cells.

    PubMed

    Prescott, Hilary M A; Manning, Craig; Gardner, Aaron; Ritchie, William A; Pizzi, Romain; Girling, Simon; Valentine, Iain; Wang, Chengdong; Jahoda, Colin A B

    2015-01-01

    Since the first mammal was cloned, the idea of using this technique to help endangered species has aroused considerable interest. However, several issues limit this possibility, including the relatively low success rate at every stage of the cloning process, and the dearth of usable tissues from these rare animals. iPS cells have been produced from cells from a number of rare mammalian species and this is the method of choice for strategies to improve cloning efficiency and create new gametes by directed differentiation. Nevertheless information about other stem cell/progenitor capabilities of cells from endangered species could prove important for future conservation approaches and adds to the knowledge base about cellular material that can be extremely limited. Multipotent progenitor cells, termed skin-derived precursor (SKP) cells, can be isolated directly from mammalian skin dermis, and human cheek tissue has also been shown to be a good source of SKP-like cells. Recently we showed that structures identical to SKPs termed m-SKPs could be obtained from monolayer/ two dimensional (2D) skin fibroblast cultures. Here we aimed to isolate m-SKPs from cultured cells of three endangered species; giant panda (Ailuropoda melanoleuca); red panda (Ailurus fulgens); and Asiatic lion (Panthera leo persica). m-SKP-like spheres were formed from the giant panda buccal mucosa fibroblasts; whereas dermal fibroblast (DF) cells cultured from abdominal skin of the other two species were unable to generate spheres. Under specific differentiation culture conditions giant panda spheres expressed neural, Schwann, adipogenic and osteogenic cell markers. Furthermore, these buccal mucosa derived spheres were shown to maintain expression of SKP markers: nestin, versican, fibronectin, and P75 and switch on expression of the stem cell marker ABCG2. These results demonstrate that giant panda cheek skin can be a useful source of m-SKP multipotent progenitors. At present lack of sample numbers

  15. Giant Panda (Ailuropoda melanoleuca) Buccal Mucosa Tissue as a Source of Multipotent Progenitor Cells

    PubMed Central

    Prescott, Hilary M. A.; Manning, Craig; Gardner, Aaron; Ritchie, William A.; Pizzi, Romain; Girling, Simon; Valentine, Iain; Wang, Chengdong; Jahoda, Colin A. B.

    2015-01-01

    Since the first mammal was cloned, the idea of using this technique to help endangered species has aroused considerable interest. However, several issues limit this possibility, including the relatively low success rate at every stage of the cloning process, and the dearth of usable tissues from these rare animals. iPS cells have been produced from cells from a number of rare mammalian species and this is the method of choice for strategies to improve cloning efficiency and create new gametes by directed differentiation. Nevertheless information about other stem cell/progenitor capabilities of cells from endangered species could prove important for future conservation approaches and adds to the knowledge base about cellular material that can be extremely limited. Multipotent progenitor cells, termed skin-derived precursor (SKP) cells, can be isolated directly from mammalian skin dermis, and human cheek tissue has also been shown to be a good source of SKP-like cells. Recently we showed that structures identical to SKPs termed m-SKPs could be obtained from monolayer/ two dimensional (2D) skin fibroblast cultures. Here we aimed to isolate m-SKPs from cultured cells of three endangered species; giant panda (Ailuropoda melanoleuca); red panda (Ailurus fulgens); and Asiatic lion (Panthera leo persica). m-SKP-like spheres were formed from the giant panda buccal mucosa fibroblasts; whereas dermal fibroblast (DF) cells cultured from abdominal skin of the other two species were unable to generate spheres. Under specific differentiation culture conditions giant panda spheres expressed neural, Schwann, adipogenic and osteogenic cell markers. Furthermore, these buccal mucosa derived spheres were shown to maintain expression of SKP markers: nestin, versican, fibronectin, and P75 and switch on expression of the stem cell marker ABCG2. These results demonstrate that giant panda cheek skin can be a useful source of m-SKP multipotent progenitors. At present lack of sample numbers

  16. Applying backpropagation neural network in the control of medullary reflex pattern

    NASA Astrophysics Data System (ADS)

    Dalcin, Bruno Luiz Galluzzi; Cruz, Frederico Alan de Oliveira; Cortez, Célia Martins; Passos, Emmanuel Lopes

    2015-12-01

    We introduced in an artificial neural network (ANN) values of the data matrix that was built with results from simulations performed with the model for the control circuit of spinal reflex presented by Dalcin et al. (2005). Standard multi-layered feed-forward backpropagation network was used to train the ANNs. Results showed that the backpropagation ANN architecture supported the specific classificatory requirements of the study.

  17. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    NASA Technical Reports Server (NTRS)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  18. How do negative emotions impair self-control? A neural model of negative urgency.

    PubMed

    Chester, David S; Lynam, Donald R; Milich, Richard; Powell, David K; Andersen, Anders H; DeWall, C Nathan

    2016-05-15

    Self-control often fails when people experience negative emotions. Negative urgency represents the dispositional tendency to experience such self-control failure in response to negative affect. Neither the neural underpinnings of negative urgency nor the more general phenomenon of self-control failure in response to negative emotions are fully understood. Previous theorizing suggests that an insufficient, inhibitory response from the prefrontal cortex may be the culprit behind such self-control failure. However, we entertained an alternative hypothesis: negative emotions lead to self-control failure because they excessively tax inhibitory regions of the prefrontal cortex. Using fMRI, we compared the neural activity of people high in negative urgency with controls on an emotional, inhibitory Go/No-Go task. While experiencing negative (but not positive or neutral) emotions, participants high in negative urgency showed greater recruitment of inhibitory brain regions than controls. Suggesting a compensatory function, inhibitory accuracy among participants high in negative urgency was associated with greater prefrontal recruitment. Greater activity in the anterior insula on negatively-valenced, inhibitory trials predicted greater substance abuse one month and one year after the MRI scan among individuals high in negative urgency. These results suggest that, among people whose negative emotions often lead to self-control failure, excessive reactivity of the brain's regulatory resources may be the culprit.

  19. How do negative emotions impair self-control? A neural model of negative urgency.

    PubMed

    Chester, David S; Lynam, Donald R; Milich, Richard; Powell, David K; Andersen, Anders H; DeWall, C Nathan

    2016-05-15

    Self-control often fails when people experience negative emotions. Negative urgency represents the dispositional tendency to experience such self-control failure in response to negative affect. Neither the neural underpinnings of negative urgency nor the more general phenomenon of self-control failure in response to negative emotions are fully understood. Previous theorizing suggests that an insufficient, inhibitory response from the prefrontal cortex may be the culprit behind such self-control failure. However, we entertained an alternative hypothesis: negative emotions lead to self-control failure because they excessively tax inhibitory regions of the prefrontal cortex. Using fMRI, we compared the neural activity of people high in negative urgency with controls on an emotional, inhibitory Go/No-Go task. While experiencing negative (but not positive or neutral) emotions, participants high in negative urgency showed greater recruitment of inhibitory brain regions than controls. Suggesting a compensatory function, inhibitory accuracy among participants high in negative urgency was associated with greater prefrontal recruitment. Greater activity in the anterior insula on negatively-valenced, inhibitory trials predicted greater substance abuse one month and one year after the MRI scan among individuals high in negative urgency. These results suggest that, among people whose negative emotions often lead to self-control failure, excessive reactivity of the brain's regulatory resources may be the culprit. PMID:26892861

  20. Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems.

    PubMed

    Shahriari kahkeshi, Maryam; Sheikholeslam, Farid; Zekri, Maryam

    2013-05-01

    This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed.

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

    NASA Astrophysics Data System (ADS)

    Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

    2009-10-01

    A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

  3. Hedgehog signalling controls zebrafish neural keel morphogenesis via its level-dependent effects on neurogenesis.

    PubMed

    Takamiya, Masanari; Campos-Ortega, Jose A

    2006-04-01

    We investigated the role of hedgehog (Hh) signalling on zebrafish neurulation, focusing on the intimate relationship between neurogenesis and morphogenesis during the neural keel stage. Through the analyses of Hh loss- and gain-of-function phenotypes, we found that Hh signalling controls the neural keel morphogenesis. To investigate underlying mechanisms, we examined cellular elongation polarity in the neural keel of Hh loss- and gain-of-function phenotypes and compared this with the deficient phenotype of a planar cell polarity (PCP) molecule, Trilobite/Strabismus. We found that Hh signalling controls cell elongation polarity of the neuroepithelium at least in part by means of PCP pathway; however, its effects are not strong enough per se to affect keel morphogenesis; instead Hh signalling mainly controls keel morphogenesis by means of affecting both medial and lateral neurogenesis. We devised a method for precise evaluation of neurogenesis in loss- and gain-of-Hh phenotypes that compensates for its delay caused by disturbed morphogenesis. We present a model that Hh signalling exerts level-dependent and binary-opposite effects on medial neurogenesis, whose modification to explain lateral neurogenesis reveals regional differences of underlying mechanisms between the two proneural domains. Such differences seem to be created in part by regional effector signalling; the effects of high Hh-signalling on medial neurogenesis can be reversed in accordance to medial Tri/Stbm level, in a polarity independent manner.

  4. Neural Substrates of Inhibitory Control Deficits in 22q11.2 Deletion Syndrome†

    PubMed Central

    Montojo, C.A.; Jalbrzikowski, M.; Congdon, E.; Domicoli, S.; Chow, C.; Dawson, C.; Karlsgodt, K.H.; Bilder, R.M.; Bearden, C.E.

    2015-01-01

    22q11.2 deletion syndrome (22q11DS) is associated with elevated levels of impulsivity, inattention, and distractibility, which may be related to underlying neurobiological dysfunction due to haploinsufficiency for genes involved in dopaminergic neurotransmission (i.e. catechol-O-methyltransferase). The Stop-signal task has been employed to probe the neural circuitry involved in response inhibition (RI); findings in healthy individuals indicate that a fronto-basal ganglia network underlies successful inhibition of a prepotent motor response. However, little is known about the neurobiological substrates of RI difficulties in 22q11DS. Here, we investigated this using functional magnetic resonance imaging while 45 adult participants (15 22q11DS patients, 30 matched controls) performed the Stop-signal task. Healthy controls showed significantly greater activation than 22q11DS patients within frontal cortical and basal ganglia regions during successful RI, whereas 22q11DS patients did not show increased neural activity relative to controls in any regions. Using the Barratt Impulsivity Scale, we also investigated whether neural dysfunction during RI was associated with cognitive impulsivity in 22q11DS patients. RI-related activity within left middle frontal gyrus and basal ganglia was associated with severity of self-reported cognitive impulsivity. These results suggest reduced engagement of RI-related brain regions in 22q11DS patients, which may be relevant to characteristic behavioral manifestations of the disorder. PMID:24177988

  5. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    PubMed

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  6. Self-control with spiking and non-spiking neural networks playing games.

    PubMed

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  7. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Technical Reports Server (NTRS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  8. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Astrophysics Data System (ADS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-12-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  9. Parietal Neural Prosthetic Control of a Computer Cursor in a Graphical-User-Interface Task

    PubMed Central

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

    2014-01-01

    Objective To date, the majority of Brain Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in Area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like “Face in a Crowd” task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the “Crowd”) using a neurally controlled cursor. We assessed whether the Crowd affected decodes of intended cursor movements by comparing it to a “Crowd Off” condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main Results Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the Crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  10. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    NASA Astrophysics Data System (ADS)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  11. FPGA-based Elman neural network control system for linear ultrasonic motor.

    PubMed

    Lin, Faa-Jeng; Hung, Ying-Chih

    2009-01-01

    A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and online learning algorithm using delta adaptation law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.

  12. Robust neural-network control of rigid-link electrically driven robots.

    PubMed

    Kwan, C; Lewis, F L; Dawson, D M

    1998-01-01

    A robust neural-network (NN) controller is proposed for the motion control of rigid-link electrically driven (RLED) robots. Two-layer NN's are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned on-line, with no off-line learning phase required. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of RLED robots without any modifications.

  13. Back propagation neural network based control for the heating system of a polysilicon reduction furnace.

    PubMed

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal I(d), which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal I(d) the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal I(d) and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

  14. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  15. Exponential Stabilization of Memristor-based Chaotic Neural Networks with Time-Varying Delays via Intermittent Control.

    PubMed

    Zhang, Guodong; Shen, Yi

    2015-07-01

    This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results.

  16. Distinct neural circuits control rhythm inhibition and spitting by the myogenic pharynx of C. elegans

    PubMed Central

    Bhatla, Nikhil; Droste, Rita; Sando, Steven R.; Huang, Anne; Horvitz, H. Robert

    2015-01-01

    SUMMARY Neural circuits have long been known to modulate myogenic muscles such as the heart, yet a mechanistic understanding at the cellular and molecular levels remains limited. We studied how light inhibits pumping of the Caenorhabditis elegans pharynx, a myogenic muscular pump for feeding, and found three neural circuits that alter pumping. First, light inhibits pumping via the I2 neuron monosynaptic circuit. Our electron microscopic reconstruction of the anterior pharynx revealed evidence for synapses from I2 onto muscle that were missing from the published connectome, and we show that these "missed synapses" are likely functional. Second, light inhibits pumping through the RIP-I1-MC neuron polysynaptic circuit, in which an inhibitory signal is likely transmitted from outside the pharynx into the pharynx in a manner analogous to how the mammalian autonomic nervous system controls the heart. Third, light causes a novel pharyngeal behavior, reversal of flow or "spitting," which is induced by the M1 neuron. These three neural circuits show that neurons can control a myogenic muscle organ not only by changing the contraction rate but also by altering the functional consequences of the contraction itself, transforming swallowing into spitting. Our observations also illustrate why connectome builders and users should be cognizant that functional synaptic connections might exist despite the absence of a declared synapse in the connectome. PMID:26212880

  17. Optogenetics and thermogenetics: technologies for controlling the activity of targeted cells within intact neural circuits

    PubMed Central

    Bernstein, Jacob G.; Garrity, Paul A.; Boyden, Edward S.

    2011-01-01

    In recent years, interest has grown in the ability to manipulate, in a temporally precise fashion, the electrical activity of specific neurons embedded within densely wired brain circuits, in order to reveal how specific neurons subserve behaviors and neural computations, and to open up new horizons on the clinical treatment of brain disorders. Technologies that enable temporally precise control of electrical activity of specific neurons, and not these neurons ’ neighbors – whose cell bodies or processes might be just tens to hundreds of nanometers away – must involve two components. First, they require as a trigger a transient pulse of energy that supports the temporal precision of the control. Second, they require a molecular sensitizer that can be expressed in specific neurons and which renders those neurons specifically responsive to the triggering energy delivered. Optogenetic tools, such as microbial opsins, can be used to activate or silence neural activity with brief pulses of light. Thermogenetic tools, such as thermosensitive TRP channels, can be used to drive neural activity downstream of increases or decreases in temperature. We here discuss the principles underlying the operation of these two recently developed, but widely used, toolboxes, as well as the directions being taken in the use and improvement of these toolboxes. PMID:22119320

  18. Hoxb1b controls oriented cell division, cell shape and microtubule dynamics in neural tube morphogenesis

    PubMed Central

    Žigman, Mihaela; Laumann-Lipp, Nico; Titus, Tom; Postlethwait, John; Moens, Cecilia B.

    2014-01-01

    Hox genes are classically ascribed to function in patterning the anterior-posterior axis of bilaterian animals; however, their role in directing molecular mechanisms underlying morphogenesis at the cellular level remains largely unstudied. We unveil a non-classical role for the zebrafish hoxb1b gene, which shares ancestral functions with mammalian Hoxa1, in controlling progenitor cell shape and oriented cell division during zebrafish anterior hindbrain neural tube morphogenesis. This is likely distinct from its role in cell fate acquisition and segment boundary formation. We show that, without affecting major components of apico-basal or planar cell polarity, Hoxb1b regulates mitotic spindle rotation during the oriented neural keel symmetric mitoses that are required for normal neural tube lumen formation in the zebrafish. This function correlates with a non-cell-autonomous requirement for Hoxb1b in regulating microtubule plus-end dynamics in progenitor cells in interphase. We propose that Hox genes can influence global tissue morphogenesis by control of microtubule dynamics in individual cells in vivo. PMID:24449840

  19. Hoxb1b controls oriented cell division, cell shape and microtubule dynamics in neural tube morphogenesis.

    PubMed

    Zigman, Mihaela; Laumann-Lipp, Nico; Titus, Tom; Postlethwait, John; Moens, Cecilia B

    2014-02-01

    Hox genes are classically ascribed to function in patterning the anterior-posterior axis of bilaterian animals; however, their role in directing molecular mechanisms underlying morphogenesis at the cellular level remains largely unstudied. We unveil a non-classical role for the zebrafish hoxb1b gene, which shares ancestral functions with mammalian Hoxa1, in controlling progenitor cell shape and oriented cell division during zebrafish anterior hindbrain neural tube morphogenesis. This is likely distinct from its role in cell fate acquisition and segment boundary formation. We show that, without affecting major components of apico-basal or planar cell polarity, Hoxb1b regulates mitotic spindle rotation during the oriented neural keel symmetric mitoses that are required for normal neural tube lumen formation in the zebrafish. This function correlates with a non-cell-autonomous requirement for Hoxb1b in regulating microtubule plus-end dynamics in progenitor cells in interphase. We propose that Hox genes can influence global tissue morphogenesis by control of microtubule dynamics in individual cells in vivo.

  20. Nuclear receptor NR5A2 controls neural stem cell fate decisions during development

    PubMed Central

    Stergiopoulos, Athanasios; Politis, Panagiotis K.

    2016-01-01

    The enormous complexity of mammalian central nervous system (CNS) is generated by highly synchronized actions of diverse factors and signalling molecules in neural stem/progenitor cells (NSCs). However, the molecular mechanisms that integrate extrinsic and intrinsic signals to control proliferation versus differentiation decisions of NSCs are not well-understood. Here we identify nuclear receptor NR5A2 as a central node in these regulatory networks and key player in neural development. Overexpression and loss-of-function experiments in primary NSCs and mouse embryos suggest that NR5A2 synchronizes cell-cycle exit with induction of neurogenesis and inhibition of astrogliogenesis by direct regulatory effects on Ink4/Arf locus, Prox1, a downstream target of proneural genes, as well as Notch1 and JAK/STAT signalling pathways. Upstream of NR5a2, proneural genes, as well as Notch1 and JAK/STAT pathways control NR5a2 endogenous expression. Collectively, these observations render NR5A2 a critical regulator of neural development and target gene for NSC-based treatments of CNS-related diseases. PMID:27447294