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.

Insights into neural crest development and evolution from genomic analysis

PubMed Central

Simões-Costa, Marcos; Bronner, Marianne E.

2013-01-01

The neural crest is an excellent model system for the study of cell type diversification during embryonic development due to its multipotency, motility, and ability to form a broad array of derivatives ranging from neurons and glia, to cartilage, bone, and melanocytes. As a uniquely vertebrate cell population, it also offers important clues regarding vertebrate origins. In the past 30 yr, introduction of recombinant DNA technology has facilitated the dissection of the genetic program controlling neural crest development and has provided important insights into gene regulatory mechanisms underlying cell migration and differentiation. More recently, new genomic approaches have provided a platform and tools that are changing the depth and breadth of our understanding of neural crest development at a “systems” level. Such advances provide an insightful view of the regulatory landscape of neural crest cells and offer a new perspective on developmental as well as stem cell and cancer biology. PMID:23817048

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. © 2014. Published by The Company of Biologists Ltd.

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.

Isolation and culture of neural crest cells from embryonic murine neural tube.

PubMed

Pfaltzgraff, Elise R; Mundell, Nathan A; Labosky, Patricia A

2012-06-02

The embryonic neural crest (NC) is a multipotent progenitor population that originates at the dorsal aspect of the neural tube, undergoes an epithelial to mesenchymal transition (EMT) and migrates throughout the embryo, giving rise to diverse cell types. NC also has the unique ability to influence the differentiation and maturation of target organs. When explanted in vitro, NC progenitors undergo self-renewal, migrate and differentiate into a variety of tissue types including neurons, glia, smooth muscle cells, cartilage and bone. NC multipotency was first described from explants of the avian neural tube. In vitro isolation of NC cells facilitates the study of NC dynamics including proliferation, migration, and multipotency. Further work in the avian and rat systems demonstrated that explanted NC cells retain their NC potential when transplanted back into the embryo. Because these inherent cellular properties are preserved in explanted NC progenitors, the neural tube explant assay provides an attractive option for studying the NC in vitro. To attain a better understanding of the mammalian NC, many methods have been employed to isolate NC populations. NC-derived progenitors can be cultured from post-migratory locations in both the embryo and adult to study the dynamics of post-migratory NC progenitors, however isolation of NC progenitors as they emigrate from the neural tube provides optimal preservation of NC cell potential and migratory properties. Some protocols employ fluorescence activated cell sorting (FACS) to isolate a NC population enriched for particular progenitors. However, when starting with early stage embryos, cell numbers adequate for analyses are difficult to obtain with FACS, complicating the isolation of early NC populations from individual embryos. Here, we describe an approach that does not rely on FACS and results in an approximately 96% pure NC population based on a Wnt1-Cre activated lineage reporter. The method presented here is adapted from

Isolation and Culture of Neural Crest Cells from Embryonic Murine Neural Tube

PubMed Central

Pfaltzgraff, Elise R.; Mundell, Nathan A.; Labosky, Patricia A.

2012-01-01

The embryonic neural crest (NC) is a multipotent progenitor population that originates at the dorsal aspect of the neural tube, undergoes an epithelial to mesenchymal transition (EMT) and migrates throughout the embryo, giving rise to diverse cell types 1-3. NC also has the unique ability to influence the differentiation and maturation of target organs4-6. When explanted in vitro, NC progenitors undergo self-renewal, migrate and differentiate into a variety of tissue types including neurons, glia, smooth muscle cells, cartilage and bone. NC multipotency was first described from explants of the avian neural tube7-9. In vitro isolation of NC cells facilitates the study of NC dynamics including proliferation, migration, and multipotency. Further work in the avian and rat systems demonstrated that explanted NC cells retain their NC potential when transplanted back into the embryo10-13. Because these inherent cellular properties are preserved in explanted NC progenitors, the neural tube explant assay provides an attractive option for studying the NC in vitro. To attain a better understanding of the mammalian NC, many methods have been employed to isolate NC populations. NC-derived progenitors can be cultured from post-migratory locations in both the embryo and adult to study the dynamics of post-migratory NC progenitors11,14-20, however isolation of NC progenitors as they emigrate from the neural tube provides optimal preservation of NC cell potential and migratory properties13,21,22. Some protocols employ fluorescence activated cell sorting (FACS) to isolate a NC population enriched for particular progenitors11,13,14,17. However, when starting with early stage embryos, cell numbers adequate for analyses are difficult to obtain with FACS, complicating the isolation of early NC populations from individual embryos. Here, we describe an approach that does not rely on FACS and results in an approximately 96% pure NC population based on a Wnt1-Cre activated lineage reporter

Discovery of a stem-like multipotent cell fate.

PubMed

Paffhausen, Emily S; Alowais, Yasir; Chao, Cara W; Callihan, Evan C; Creswell, Karen; Bracht, John R

2018-01-01

Adipose derived stem cells (ASCs) can be obtained from lipoaspirates and induced in vitro to differentiate into bone, cartilage, and fat. Using this powerful model system we show that after in vitro adipose differentiation a population of cells retain stem-like qualities including multipotency. They are lipid (-), retain the ability to propagate, express two known stem cell markers, and maintain the capacity for trilineage differentiation into chondrocytes, adipocytes, and osteoblasts. However, these cells are not traditional stem cells because gene expression analysis showed an overall expression profile similar to that of adipocytes. In addition to broadening our understanding of cellular multipotency, our work may be particularly relevant to obesity-associated metabolic disorders. The adipose expandability hypothesis proposes that inability to differentiate new adipocytes is a primary cause of metabolic syndrome in obesity, including diabetes and cardiovascular disease. Here we have defined a differentiation-resistant stem-like multipotent cell population that may be involved in regulation of adipose expandability in vivo and may therefore play key roles in the comorbidities of obesity.

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.

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.

Changing the Properties of Multipotent Mesenchymal Stromal Cells by IFNγ Administration.

PubMed

Petinati, N A; Kapranov, N M; Bigil'deev, A E; Popova, M D; Davydova, Yu O; Gal'tseva, I V; Drize, N I; Kuz'mina, L A; Parovichnikova, E N; Savchenko, V G

2017-06-01

We studied changes in the population of human multipotent mesenchymal stromal cells activated by IFNγ. The cells were cultured under standard conditions; IFNγ was added in various concentrations for 4 h or over 2 passages. It was shown that the total cell production significantly decreased after long-term culturing with IFNγ, but 4-h exposure did not affect this parameter. After 4-h culturing, the expression levels of IDO1, CSF1, and IL-6 increased by 300, 7, and 2.4 times, respectively, and this increase persisted 1 and 2 days after removal of IFNγ from the culture medium. The expression of class I and II MHC (HLA) on cell surface practically did not change immediately after exposure to IFNγ, but during further culturing, HLA-ABC (MHC I) and HLA-DR (MHC II) expression significantly increased, which abolished the immune privilege in these cells, the property allowing clinical use of allogenic multipotent mesenchymal stromal cells. Multipotent mesenchymal stromal cells can suppress proliferation of lymphocytes. The degree of this suppression depends on individual properties of multipotent mesenchymal stromal cell donor. Treatment with IFNγ did not significantly affect the intensity of inhibition of lymphocyte proliferation by these cells.

Neural control of magnetic suspension systems

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1993-01-01

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

Neural 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.

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.

Novel Regenerative Therapies Based on Regionally Induced Multipotent Stem Cells in Post-Stroke Brains: Their Origin, Characterization, and Perspective.

PubMed

Takagi, Toshinori; Yoshimura, Shinichi; Sakuma, Rika; Nakano-Doi, Akiko; Matsuyama, Tomohiro; Nakagomi, Takayuki

2017-12-01

Brain injuries such as ischemic stroke cause severe neural loss. Until recently, it was believed that post-ischemic areas mainly contain necrotic tissue and inflammatory cells. However, using a mouse model of cerebral infarction, we demonstrated that stem cells develop within ischemic areas. Ischemia-induced stem cells can function as neural progenitors; thus, we initially named them injury/ischemia-induced neural stem/progenitor cells (iNSPCs). However, because they differentiate into more than neural lineages, we now refer to them as ischemia-induced multipotent stem cells (iSCs). Very recently, we showed that putative iNSPCs/iSCs are present within post-stroke areas in human brains. Because iNSPCs/iSCs isolated from mouse and human ischemic tissues can differentiate into neuronal lineages in vitro, it is possible that a clearer understanding of iNSPC/iSC profiles and the molecules that regulate iNSPC/iSC fate (e.g., proliferation, differentiation, and survival) would make it possible to perform neural regeneration/repair in patients following stroke. In this article, we introduce the origin and traits of iNSPCs/iSCs based on our reports and recent viewpoints. We also discuss their possible contribution to neurogenesis through endogenous and exogenous iNSPC/iSC therapies following ischemic stroke.

Neural crest cells: from developmental biology to clinical interventions.

PubMed

Noisa, Parinya; Raivio, Taneli

2014-09-01

Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

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.

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

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.

Isolation and Characterization of Canine Amniotic Membrane-Derived Multipotent Stem Cells

PubMed Central

Kim, Hyung-Sik; Kang, Kyung-Sun

2012-01-01

Recent studies have shown that amniotic membrane tissue is a rich source of stem cells in humans. In clinical applications, the amniotic membrane tissue had therapeutic effects on wound healing and corneal surface reconstruction. Here, we successfully isolated and identified multipotent stem cells (MSCs) from canine amniotic membrane tissue. We cultured the canine amniotic membrane-derived multipotent stem cells (cAM-MSCs) in low glucose DMEM medium. cAM-MSCs have a fibroblast-like shape and adhere to tissue culture plastic. We characterized the immunophenotype of cAM-MSCs by flow cytometry and measured cell proliferation by the cumulative population doubling level (CPDL). We performed differentiation studies for the detection of trilineage multipotent ability, under the appropriate culture conditions. Taken together, our results show that cAM-MSCs could be a rich source of stem cells in dogs. Furthermore, cAM-MSCs may be useful as a cell therapy application for veterinary regenerative medicine. PMID:23024756

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal

Msx1-Positive Progenitors in the Retinal Ciliary Margin Give Rise to Both Neural and Non-neural Progenies in Mammals.

PubMed

Bélanger, Marie-Claude; Robert, Benoit; Cayouette, Michel

2017-01-23

In lower vertebrates, stem/progenitor cells located in a peripheral domain of the retina, called the ciliary margin zone (CMZ), cooperate with retinal domain progenitors to build the mature neural retina. In mammals, it is believed that the CMZ lacks neurogenic potential and that the retina develops from one pool of multipotent retinal progenitor cells (RPCs). Here we identify a population of Msx1-expressing progenitors in the mouse CMZ that is both molecularly and functionally distinct from RPCs. Using genetic lineage tracing, we report that Msx1 progenitors have unique developmental properties compared with RPCs. Msx1 lineages contain both neural retina and non-neural ciliary epithelial progenies and overall generate fewer photoreceptors than classical RPC lineages. Furthermore, we show that the endocytic adaptor protein Numb regulates the balance between neural and non-neural fates in Msx1 progenitors. These results uncover a population of CMZ progenitors, distinct from classical RPCs, that also contributes to mammalian retinogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

EZ spheres: a stable and expandable culture system for the generation of pre-rosette multipotent stem cells from human ESCs and iPSCs.

PubMed

Ebert, Allison D; Shelley, Brandon C; Hurley, Amanda M; Onorati, Marco; Castiglioni, Valentina; Patitucci, Teresa N; Svendsen, Soshana P; Mattis, Virginia B; McGivern, Jered V; Schwab, Andrew J; Sareen, Dhruv; Kim, Ho Won; Cattaneo, Elena; Svendsen, Clive N

2013-05-01

We have developed a simple method to generate and expand multipotent, self-renewing pre-rosette neural stem cells from both human embryonic stem cells (hESCs) and human induced pluripotent stem cells (iPSCs) without utilizing embryoid body formation, manual selection techniques, or complex combinations of small molecules. Human ESC and iPSC colonies were lifted and placed in a neural stem cell medium containing high concentrations of EGF and FGF-2. Cell aggregates (termed EZ spheres) could be expanded for long periods using a chopping method that maintained cell-cell contact. Early passage EZ spheres rapidly down-regulated OCT4 and up-regulated SOX2 and nestin expression. They retained the potential to form neural rosettes and consistently differentiated into a range of central and peripheral neural lineages. Thus, they represent a very early neural stem cell with greater differentiation flexibility than other previously described methods. As such, they will be useful for the rapidly expanding field of neurological development and disease modeling, high-content screening, and regenerative therapies based on pluripotent stem cell technology. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Exfoliated Human Olfactory Neuroepithelium: A Source of Neural Progenitor Cells.

PubMed

Jiménez-Vaca, Ana L; Benitez-King, Gloria; Ruiz, Víctor; Ramírez-Rodríguez, Gerardo B; Hernández-de la Cruz, Beatriz; Salamanca-Gómez, Fabio A; González-Márquez, Humberto; Ramírez-Sánchez, Israel; Ortíz-López, Leonardo; Vélez-Del Valle, Cristina; Ordoñez-Razo, Rosa Ma

2018-03-01

Neural progenitor cells (NPC) contained in the human adult olfactory neuroepithelium (ONE) possess an undifferentiated state, the capability of self-renewal, the ability to generate neural and glial cells as well as being kept as neurospheres in cell culture conditions. Recently, NPC have been isolated from human or animal models using high-risk surgical methods. Therefore, it was necessary to improve methodologies to obtain and maintain human NPC as well as to achieve better knowledge of brain disorders. In this study, we propose the establishment and characterization of NPC cultures derived from the human olfactory neuroepithelium, using non-invasive procedures. Twenty-two healthy individuals (29.7 ± 4.5 years of age) were subjected to nasal exfoliation. Cells were recovered and kept as neurospheres under serum-free conditions. The neural progenitor origin of these neurospheres was determined by immunocytochemistry and qPCR. Their ability for self-renewal and multipotency was analyzed by clonogenic and differentiation assays, respectively. In the cultures, the ONE cells preserved the phenotype of the neurospheres. The expression levels of Nestin, Musashi, Sox2, and βIII-tubulin demonstrated the neural origin of the neurospheres; 48% of the cells separated could generate neurospheres, determining that they retained their self-renewal capacity. Neurospheres were differentiated in the absence of growth factors (EGF and FGF), and their multipotency ability was maintained as well. We were also able to isolate and grow human neural progenitor cells (neurospheres) through nasal exfoliates (non-invasive method) of the ONE from healthy adults, which is an extremely important contribution for the study of brain disorders and for the development of new therapies.

Dynamic equilibrium of heterogeneous and interconvertible multipotent hematopoietic cell subsets.

PubMed

Weston, Wendy; Zayas, Jennifer; Perez, Ruben; George, John; Jurecic, Roland

2014-06-06

Populations of hematopoietic stem cells and progenitors are quite heterogeneous and consist of multiple cell subsets with distinct phenotypic and functional characteristics. Some of these subsets also appear to be interconvertible and oscillate between functionally distinct states. The multipotent hematopoietic cell line EML has emerged as a unique model to study the heterogeneity and interconvertibility of multipotent hematopoietic cells. Here we describe extensive phenotypic and functional heterogeneity of EML cells which stems from the coexistence of multiple cell subsets. Each of these subsets is phenotypically and functionally heterogeneous, and displays distinct multilineage differentiation potential, cell cycle profile, proliferation kinetics, and expression pattern of HSC markers and some of the key lineage-associated transcription factors. Analysis of their maintenance revealed that on a population level all EML cell subsets exhibit cell-autonomous interconvertible properties, with the capacity to generate all other subsets and re-establish complete parental EML cell population. Moreover, all EML cell subsets generated during multiple cell generations maintain their distinct phenotypic and functional signatures and interconvertible properties. The model of EML cell line suggests that interconvertible multipotent hematopoietic cell subsets coexist in a homeostatically maintained dynamic equilibrium which is regulated by currently unknown cell-intrinsic mechanisms.

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.

Production and characterization of immortal human neural stem cell line with multipotent differentiation property.

PubMed

Kim, Seung U; Nagai, Atsushi; Nakagawa, Eiji; Choi, Hyun B; Bang, Jung H; Lee, Hong J; Lee, Myung A; Lee, Yong B; Park, In H

2008-01-01

We document the protocols and methods for the production of immortalized cell lines of human neural stem cells from the human fetal central nervous system (CNS) cells by using a retroviral vector encoding v-myc oncogene. One of the human neural stem cell lines (HB1.F3) was found to express nestin and other specific markers for human neural stem cells, giving rise to three fundamental cell types of the CNS: neurons, astrocytes, and oligodendrocytes. After transplantation into the brain of mouse model of stroke, implanted human neural stem cells were observed to migrate extensively from the site of implantation into other anatomical sites and to differentiate into neurons and glial cells.

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.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

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

Simultaneous inhibition of multiple oncogenic miRNAs by a multi-potent microRNA sponge.

PubMed

Jung, Jaeyun; Yeom, Chanjoo; Choi, Yeon-Sook; Kim, Sinae; Lee, EunJi; Park, Min Ji; Kang, Sang Wook; Kim, Sung Bae; Chang, Suhwan

2015-08-21

The roles of oncogenic miRNAs are widely recognized in many cancers. Inhibition of single miRNA using antagomiR can efficiently knock-down a specific miRNA. However, the effect is transient and often results in subtle phenotype, as there are other miRNAs contribute to tumorigenesis. Here we report a multi-potent miRNA sponge inhibiting multiple miRNAs simultaneously. As a model system, we targeted miR-21, miR-155 and miR-221/222, known as oncogenic miRNAs in multiple tumors including breast and pancreatic cancers. To achieve efficient knockdown, we generated perfect and bulged-matched miRNA binding sites (MBS) and introduced multiple copies of MBS, ranging from one to five, in the multi-potent miRNA sponge. Luciferase reporter assay showed the multi-potent miRNA sponge efficiently inhibited 4 miRNAs in breast and pancreatic cancer cells. Furthermore, a stable and inducible version of the multi-potent miRNA sponge cell line showed the miRNA sponge efficiently reduces the level of 4 target miRNAs and increase target protein level of these oncogenic miRNAs. Finally, we showed the miRNA sponge sensitize cells to cancer drug and attenuate cell migratory activity. Altogether, our study demonstrates the multi-potent miRNA sponge is a useful tool to examine the functional impact of simultaneous inhibition of multiple miRNAs and proposes a therapeutic potential.

Osteoarthritis-derived chondrocytes are a potential source of multipotent progenitor cells for cartilage tissue engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oda, Tomoyuki; Sakai, Tadahiro; Hiraiwa, Hideki

The natural healing capacity of damaged articular cartilage is poor, rendering joint surface injuries a prime target for regenerative medicine. While autologous chondrocyte or mesenchymal stem cell (MSC) implantation can be applied to repair cartilage defects in young patients, no appropriate long-lasting treatment alternative is available for elderly patients with osteoarthritis (OA). Multipotent progenitor cells are reported to present in adult human articular cartilage, with a preponderance in OA cartilage. These facts led us to hypothesize the possible use of osteoarthritis-derived chondrocytes as a cell source for cartilage tissue engineering. We therefore analyzed chondrocyte- and stem cell-related markers, cell growthmore » rate, and multipotency in OA chondrocytes (OACs) and bone marrow-derived MSCs, along with normal articular chondrocytes (ACs) as a control. OACs demonstrated similar phenotype and proliferation rate to MSCs. Furthermore, OACs exhibited multilineage differentiation ability with a greater chondrogenic differentiation ability than MSCs, which was equivalent to ACs. We conclude that chondrogenic capacity is not significantly affected by OA, and OACs could be a potential source of multipotent progenitor cells for cartilage tissue engineering. - Highlights: • Osteoarthritis chondrocytes (OACs) have multilineage differentiation capacity. • Articular chondrocytes (ACs) and OACs have similar gene expression profiles. • OACs have high chondrogenic potential. • OACs could be a cell resource for cartilage tissue engineering.« less

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

Control of autonomous robot using neural networks

NASA Astrophysics Data System (ADS)

Barton, Adam; Volna, Eva

2017-07-01

The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

Dynamic equilibrium of heterogeneous and interconvertible multipotent hematopoietic cell subsets

PubMed Central

Weston, Wendy; Zayas, Jennifer; Perez, Ruben; George, John; Jurecic, Roland

2014-01-01

Populations of hematopoietic stem cells and progenitors are quite heterogeneous and consist of multiple cell subsets with distinct phenotypic and functional characteristics. Some of these subsets also appear to be interconvertible and oscillate between functionally distinct states. The multipotent hematopoietic cell line EML has emerged as a unique model to study the heterogeneity and interconvertibility of multipotent hematopoietic cells. Here we describe extensive phenotypic and functional heterogeneity of EML cells which stems from the coexistence of multiple cell subsets. Each of these subsets is phenotypically and functionally heterogeneous, and displays distinct multilineage differentiation potential, cell cycle profile, proliferation kinetics, and expression pattern of HSC markers and some of the key lineage-associated transcription factors. Analysis of their maintenance revealed that on a population level all EML cell subsets exhibit cell-autonomous interconvertible properties, with the capacity to generate all other subsets and re-establish complete parental EML cell population. Moreover, all EML cell subsets generated during multiple cell generations maintain their distinct phenotypic and functional signatures and interconvertible properties. The model of EML cell line suggests that interconvertible multipotent hematopoietic cell subsets coexist in a homeostatically maintained dynamic equilibrium which is regulated by currently unknown cell-intrinsic mechanisms. PMID:24903657

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.

Low-Oxygen Culture Conditions Extend the Multipotent Properties of Human Retinal Progenitor Cells

PubMed Central

Tucker, Budd A.; Young, Michael J.

2014-01-01

Purpose: Development of an effective cell-based therapy is highly dependent upon having a reproducible cell source suitable for transplantation. One potential source, isolated from the developing fetal neural retina, is the human retinal progenitor cell (hRPC). One limiting factor for the use of hRPCs is their in vitro expansion limit. As such, the aim of this study was to determine whether culturing hRPCs under 3% O2 would support their proliferative capacity while maintaining multipotency. Methods: To determine the effect of low oxygen on the ability of hRPCs to self-renew, rates of proliferation and apoptosis, telomerase activity, and expression of proliferative, stemness, and differentiation markers were assessed for hRPCs cultured in 3% and 20% oxygen conditions. Results: Culture under 3% oxygen increases the proliferation rate and shifts the proliferation limit of hRPCs to greater 40 divisions. This increased capacity for proliferation is correlated with an upregulation of Ki67, CyclinD1, and telomerase activity and a decrease in p53 expression and apoptosis. Increased expression of cMyc, Klf4, Oct4, and Sox2 in 3% O2 is correlated with stabilization of both HIF1α and HIF2α. The eye field development markers Pax6, Sox2, and Otx2 are present in hRPCs up to passage 16 in 3% O2. Following in vitro differentiation hRPCs expanded in the 3% O2 were able to generate specialized retinal cells, including rods and cones. Conclusions: Low-oxygen culture conditions act to maintain both multipotency and self-renewal properties of hRPCs in vitro. The extended expansion limits permit the development of a clinical-grade reagent for transplantation. PMID:24320879

WNT/β-catenin signaling mediates human neural crest induction via a pre-neural border intermediate.

PubMed

Leung, Alan W; Murdoch, Barbara; Salem, Ahmed F; Prasad, Maneeshi S; Gomez, Gustavo A; García-Castro, Martín I

2016-02-01

Neural crest (NC) cells arise early in vertebrate development, migrate extensively and contribute to a diverse array of ectodermal and mesenchymal derivatives. Previous models of NC formation suggested derivation from neuralized ectoderm, via meso-ectodermal, or neural-non-neural ectoderm interactions. Recent studies using bird and amphibian embryos suggest an earlier origin of NC, independent of neural and mesodermal tissues. Here, we set out to generate a model in which to decipher signaling and tissue interactions involved in human NC induction. Our novel human embryonic stem cell (ESC)-based model yields high proportions of multipotent NC cells (expressing SOX10, PAX7 and TFAP2A) in 5 days. We demonstrate a crucial role for WNT/β-catenin signaling in launching NC development, while blocking placodal and surface ectoderm fates. We provide evidence of the delicate temporal effects of BMP and FGF signaling, and find that NC development is separable from neural and/or mesodermal contributions. We further substantiate the notion of a neural-independent origin of NC through PAX6 expression and knockdown studies. Finally, we identify a novel pre-neural border state characterized by early WNT/β-catenin signaling targets that displays distinct responses to BMP and FGF signaling from the traditional neural border genes. In summary, our work provides a fast and efficient protocol for human NC differentiation under signaling constraints similar to those identified in vivo in model organisms, and strengthens a framework for neural crest ontogeny that is separable from neural and mesodermal fates. © 2016. Published by The Company of Biologists Ltd.

The origin and evolution of the neural crest

PubMed Central

Donoghue, Philip C. J.; Graham, Anthony; Kelsh, Robert N.

2009-01-01

Summary Many of the features that distinguish the vertebrates from other chordates are derived from the neural crest, and it has long been argued that the emergence of this multipotent embryonic population was a key innovation underpinning vertebrate evolution. More recently, however, a number of studies have suggested that the evolution of the neural crest was less sudden than previously believed. This has exposed the fact that neural crest, as evidenced by its repertoire of derivative cell types, has evolved through vertebrate evolution. In this light, attempts to derive a typological definition of neural crest, in terms of molecular signatures or networks, are unfounded. We propose a less restrictive, embryological definition of this cell type that facilitates, rather than precludes, investigating the evolution of neural crest. While the evolutionary origin of neural crest has attracted much attention, its subsequent evolution has received almost no attention and yet it is more readily open to experimental investigation and has greater relevance to understanding vertebrate evolution. Finally, we provide a brief outline of how the evolutionary emergence of neural crest potentiality may have proceeded, and how it may be investigated. PMID:18478530

Application of neural models as controllers in mobile robot velocity control loop

NASA Astrophysics Data System (ADS)

Cerkala, Jakub; Jadlovska, Anna

2017-01-01

This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

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.

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.

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.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Compliance control with embedded neural elements

NASA Technical Reports Server (NTRS)

Venkataraman, S. T.; Gulati, S.

1992-01-01

The authors discuss a control approach that embeds the neural elements within a model-based compliant control architecture for robotic tasks that involve contact with unstructured environments. Compliance control experiments have been performed on actual robotics hardware to demonstrate the performance of contact control schemes with neural elements. System parameters were identified under the assumption that environment dynamics have a fixed nonlinear structure. A robotics research arm, placed in contact with a single degree-of-freedom electromechanical environment dynamics emulator, was commanded to move through a desired trajectory. The command was implemented by using a compliant control strategy.

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

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

2018-08-01

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

Multipotent progenitor cells are present in human peripheral blood.

PubMed

Cesselli, Daniela; Beltrami, Antonio Paolo; Rigo, Silvia; Bergamin, Natascha; D'Aurizio, Federica; Verardo, Roberto; Piazza, Silvano; Klaric, Enio; Fanin, Renato; Toffoletto, Barbara; Marzinotto, Stefania; Mariuzzi, Laura; Finato, Nicoletta; Pandolfi, Maura; Leri, Annarosa; Schneider, Claudio; Beltrami, Carlo Alberto; Anversa, Piero

2009-05-22

To determine whether the peripheral blood in humans contains a population of multipotent progenitor cells (MPCs), products of leukapheresis were obtained from healthy donor volunteers following the administration of granulocyte colony-stimulating factor. Small clusters of adherent proliferating cells were collected, and these cells continued to divide up to 40 population doublings without reaching replicative senescence and growth arrest. MPCs were positive for the transcription factors Nanog, Oct3/4, Sox2, c-Myc, and Klf4 and expressed several antigens characteristic of mesenchymal stem cells. However, they were negative for markers of hematopoietic stem/progenitor cells and bone marrow cell lineages. MPCs had a cloning efficiency of approximately 3%, and following their expansion, retained a highly immature phenotype. Under permissive culture conditions, MPCs differentiated into neurons, glial cells, hepatocytes, cardiomyocytes, endothelial cells, and osteoblasts. Moreover, the gene expression profile of MPCs partially overlapped with that of neural and embryonic stem cells, further demonstrating their primitive, uncommitted phenotype. Following subcutaneous transplantation in nonimmunosuppressed mice, MPCs migrated to distant organs and integrated structurally and functionally within the new tissue, acquiring the identity of resident parenchymal cells. In conclusion, undifferentiated cells with properties of embryonic stem cells can be isolated and expanded from human peripheral blood after granulocyte colony-stimulating factor administration. This cell pool may constitute a unique source of autologous cells with critical clinical import.

Neural Networks for Modeling and Control of Particle Accelerators

NASA Astrophysics Data System (ADS)

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

2016-04-01

Particle accelerators are host to myriad nonlinear and complex physical phenomena. 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, 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. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. 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 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.

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.

The application of neural network PID controller to control the light gasoline etherification

NASA Astrophysics Data System (ADS)

Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

2017-06-01

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

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

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

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.

Entropy, Ergodicity, and Stem Cell Multipotency

NASA Astrophysics Data System (ADS)

Ridden, Sonya J.; Chang, Hannah H.; Zygalakis, Konstantinos C.; MacArthur, Ben D.

2015-11-01

Populations of mammalian stem cells commonly exhibit considerable cell-cell variability. However, the functional role of this diversity is unclear. Here, we analyze expression fluctuations of the stem cell surface marker Sca1 in mouse hematopoietic progenitor cells using a simple stochastic model and find that the observed dynamics naturally lie close to a critical state, thereby producing a diverse population that is able to respond rapidly to environmental changes. We propose an information-theoretic interpretation of these results that views cellular multipotency as an instance of maximum entropy statistical inference.

PSA-NCAM-Negative Neural Crest Cells Emerging during Neural Induction of Pluripotent Stem Cells Cause Mesodermal Tumors and Unwanted Grafts

PubMed Central

Lee, Dongjin R.; Yoo, Jeong-Eun; Lee, Jae Souk; Park, Sanghyun; Lee, Junwon; Park, Chul-Yong; Ji, Eunhyun; Kim, Han-Soo; Hwang, Dong-Youn; Kim, Dae-Sung; Kim, Dong-Wook

2015-01-01

Summary Tumorigenic potential of human pluripotent stem cells (hPSCs) is an important issue in clinical applications. Despite many efforts, PSC-derived neural precursor cells (NPCs) have repeatedly induced tumors in animal models even though pluripotent cells were not detected. We found that polysialic acid-neural cell adhesion molecule (PSA-NCAM)− cells among the early NPCs caused tumors, whereas PSA-NCAM+ cells were nontumorigenic. Molecular profiling, global gene analysis, and multilineage differentiation of PSA-NCAM− cells confirm that they are multipotent neural crest stem cells (NCSCs) that could differentiate into both ectodermal and mesodermal lineages. Transplantation of PSA-NCAM− cells in a gradient manner mixed with PSA-NCAM+ cells proportionally increased mesodermal tumor formation and unwanted grafts such as PERIPHERIN+ cells or pigmented cells in the rat brain. Therefore, we suggest that NCSCs are a critical target for tumor prevention in hPSC-derived NPCs, and removal of PSA-NCAM− cells eliminates the tumorigenic potential originating from NCSCs after transplantation. PMID:25937368

Effects of Combined Transplantation of Multipotent Mesenchymal Stromal and Hemopoietic Stem Cells on Regeneration of the Hemopoietic Tissue.

PubMed

Maklakova, I Yu; Grebnev, D Yu

2017-05-01

The effect of allogenic combined transplantation of placental multipotent mesenchymal stromal and hemopoietic stem cells on regeneration of the myeloid tissue and spleen after acute blood loss was studied in laboratory mice. Combined transplantation of these cells did not change the content of cytogenetically modified cells in the bone marrow under normal conditions, but reduced their levels after acute blood loss. Combined transplantation of multipotent mesenchymal stromal and hemopoietic stem cells promoted activation of erythropoiesis and granulocytopoiesis. The major morphometric and cytological parameters of the white pulp of the spleen decreased, presumably due to immunosuppressive effect of multipotent mesenchymal stromal cells.

Neural Networks for Modeling and Control of Particle Accelerators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.

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

Neural Networks for Modeling and Control of Particle Accelerators

DOE PAGES

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; ...

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

Requirement for Foxd3 in the maintenance of neural crest progenitors.

PubMed

Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

2008-05-01

Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

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.

System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

Neural controller for adaptive movements with unforeseen payloads.

PubMed

Kuperstein, M; Wang, J

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% 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.

The transcription factor Nerfin-1 prevents reversion of neurons into neural stem cells.

PubMed

Froldi, Francesca; Szuperak, Milan; Weng, Chen-Fang; Shi, Wei; Papenfuss, Anthony T; Cheng, Louise Y

2015-01-15

Cellular dedifferentiation is the regression of a cell from a specialized state to a more multipotent state and is implicated in cancer. However, the transcriptional network that prevents differentiated cells from reacquiring stem cell fate is so far unclear. Neuroblasts (NBs), the Drosophila neural stem cells, are a model for the regulation of stem cell self-renewal and differentiation. Here we show that the Drosophila zinc finger transcription factor Nervous fingers 1 (Nerfin-1) locks neurons into differentiation, preventing their reversion into NBs. Following Prospero-dependent neuronal specification in the ganglion mother cell (GMC), a Nerfin-1-specific transcriptional program maintains differentiation in the post-mitotic neurons. The loss of Nerfin-1 causes reversion to multipotency and results in tumors in several neural lineages. Both the onset and rate of neuronal dedifferentiation in nerfin-1 mutant lineages are dependent on Myc- and target of rapamycin (Tor)-mediated cellular growth. In addition, Nerfin-1 is required for NB differentiation at the end of neurogenesis. RNA sequencing (RNA-seq) and chromatin immunoprecipitation (ChIP) analysis show that Nerfin-1 administers its function by repression of self-renewing-specific and activation of differentiation-specific genes. Our findings support the model of bidirectional interconvertibility between neural stem cells and their post-mitotic progeny and highlight the importance of the Nerfin-1-regulated transcriptional program in neuronal maintenance. © 2015 Froldi et al.; Published by Cold Spring Harbor Laboratory Press.

Hyaluronic Acid (HA) Scaffolds and Multipotent Stromal Cells (MSCs) in Regenerative Medicine.

PubMed

Prè, Elena Dai; Conti, Giamaica; Sbarbati, Andrea

2016-12-01

Traditional methods for tissue regeneration commonly used synthetic scaffolds to regenerate human tissues. However, they had several limitations, such as foreign body reactions and short time duration. In order to overcome these problems, scaffolds made of natural polymers are preferred. One of the most suitable and widely used materials to fabricate these scaffolds is hyaluronic acid. Hyaluronic acid is the primary component of the extracellular matrix of the human connective tissue. It is an ideal material for scaffolds used in tissue regeneration, thanks to its properties of biocompatibility, ease of chemical functionalization and degradability. In the last few years, especially from 2010, scientists have seen that the cell-based engineering of these natural scaffolds allows obtaining even better results in terms of tissue regeneration and the research started to grow in this direction. Multipotent stromal cells, also known as mesenchymal stem cells, plastic-adherent cells isolated from bone marrow and other mesenchymal tissues, with self-renew and multi-potency properties are ideal candidates for this aim. Normally, they are pre-seeded onto these scaffolds before their implantation in vivo. This review discusses the use of hyaluronic acid-based scaffolds together with multipotent stromal cells, as a very promising tool in regenerative medicine.

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.

Requirement for Foxd3 in Maintenance of Neural Crest Progenitors

PubMed Central

Teng, Lu; Mundell, Nathan A.; Frist, Audrey Y.; Wang, Qiaohong; Labosky, Patricia A.

2008-01-01

Summary Understanding the molecular mechanisms of stem cell maintenance is critical for the ultimate goal of manipulating stem cells for treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3−/− embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue specific deletion of Foxd3 in the neural crest, we show that Foxd3flox/−; Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo. PMID:18367558

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control

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

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

ER fluid applications to vibration control devices and an adaptive neural-net controller

NASA Astrophysics Data System (ADS)

Morishita, Shin; Ura, Tamaki

1993-07-01

Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

Neural crest specification and migration independently require NSD3-related lysine methyltransferase activity

PubMed Central

Jacques-Fricke, Bridget T.; Gammill, Laura S.

2014-01-01

Neural crest precursors express genes that cause them to become migratory, multipotent cells, distinguishing them from adjacent stationary neural progenitors in the neurepithelium. Histone methylation spatiotemporally regulates neural crest gene expression; however, the protein methyltransferases active in neural crest precursors are unknown. Moreover, the regulation of methylation during the dynamic process of neural crest migration is unclear. Here we show that the lysine methyltransferase NSD3 is abundantly and specifically expressed in premigratory and migratory neural crest cells. NSD3 expression commences before up-regulation of neural crest genes, and NSD3 is necessary for expression of the neural plate border gene Msx1, as well as the key neural crest transcription factors Sox10, Snail2, Sox9, and FoxD3, but not gene expression generally. Nevertheless, only Sox10 histone H3 lysine 36 dimethylation requires NSD3, revealing unexpected complexity in NSD3-dependent neural crest gene regulation. In addition, by temporally limiting expression of a dominant negative to migratory stages, we identify a novel, direct requirement for NSD3-related methyltransferase activity in neural crest migration. These results identify NSD3 as the first protein methyltransferase essential for neural crest gene expression during specification and show that NSD3-related methyltransferase activity independently regulates migration. PMID:25318671

Proteomic Cornerstones of Hematopoietic Stem Cell Differentiation: Distinct Signatures of Multipotent Progenitors and Myeloid Committed Cells*

PubMed Central

Klimmeck, Daniel; Hansson, Jenny; Raffel, Simon; Vakhrushev, Sergey Y.; Trumpp, Andreas; Krijgsveld, Jeroen

2012-01-01

Regenerative tissues such as the skin epidermis, the intestinal mucosa or the hematopoietic system are organized in a hierarchical manner with stem cells building the top of this hierarchy. Somatic stem cells harbor the highest self-renewal activity and generate a series of multipotent progenitors which differentiate into lineage committed progenitors and subsequently mature cells. In this report, we applied an in-depth quantitative proteomic approach to analyze and compare the full proteomes of ex vivo isolated and FACS-sorted populations highly enriched for either multipotent hematopoietic stem/progenitor cells (HSPCs, LinnegSca-1+c-Kit+) or myeloid committed precursors (LinnegSca-1−c-Kit+). By employing stable isotope dimethyl labeling and high-resolution mass spectrometry, more than 5000 proteins were quantified. From biological triplicate experiments subjected to rigorous statistical evaluation, 893 proteins were found differentially expressed between multipotent and myeloid committed cells. The differential protein content in these cell populations points to a distinct structural organization of the cytoskeleton including remodeling activity. In addition, we found a marked difference in the expression of metabolic enzymes, including a clear shift of specific protein isoforms of the glycolytic pathway. Proteins involved in translation showed a collective higher expression in myeloid progenitors, indicating an increased translational activity. Strikingly, the data uncover a unique signature related to immune defense mechanisms, centering on the RIG-I and type-1 interferon response systems, which are installed in multipotent progenitors but not evident in myeloid committed cells. This suggests that specific, and so far unrecognized, mechanisms protect these immature cells before they mature. In conclusion, this study indicates that the transition of hematopoietic stem/progenitors toward myeloid commitment is accompanied by a profound change in processing of

Neck muscle biomechanics and neural control.

PubMed

Fice, Jason Bradley; Siegmund, Gunter P; Blouin, Jean-Sebastien

2018-04-18

The mechanics, morphometry, and geometry of our joints, segments and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate if the biomechanical actions of individual neck muscles predicts their neural control. Specifically, we compared the moment direction & variability produced by electrical stimulation of a neck muscle (biomechanics) to their preferred activation direction & variability (neural control). Subjects sat upright with their head fixed to a 6-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from RMS EMG when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 3D directions. The spatial tuning curves at 15% MVC were well-defined (unimodal, p<0.05) and their preferred directions were 23, 39, & 21{degree sign} different from their electrically stimulated directions for the SCM, SPL, and SSC respectively (p<0.05). Intra-subject variability was smaller in electrically stimulated moment directions when compared to voluntary preferred directions, and intra-subject variability decreased with increased activation levels. Our findings show that the neural control of neck muscles is not based solely on optimizing individual muscle biomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles.

A novel, immortal, and multipotent human neural stem cell line generating functional neurons and oligodendrocytes.

PubMed

De Filippis, Lidia; Lamorte, Giuseppe; Snyder, Evan Y; Malgaroli, Antonio; Vescovi, Angelo L

2007-09-01

The discovery and study of neural stem cells have revolutionized our understanding of the neurogenetic process, and their inherent ability to adopt expansive growth behavior in vitro is of paramount importance for the development of novel therapeutics based on neural cell replacement. Recent advances in high-throughput assays for drug development and gene discovery dictate the need for rapid, reproducible, long-term expansion of human neural stem cells (hNSCs). In this view, the complement of wild-type cell lines currently available is insufficient. Here we report the establishment of a stable human neural stem cell line (immortalized human NSCs [IhNSCs]) by v-myc-mediated immortalization of previously derived wild-type hNSCs. These cells demonstrate three- to fourfold faster proliferation than wild-type cells in response to growth factors but retain rather similar properties, including multipotentiality. By molecular biology, biochemistry, immunocytochemistry, fluorescence microscopy, and electrophysiology, we show that upon growth factor removal, IhNSCs completely downregulate v-myc expression, cease proliferation, and differentiate terminally into three major neural lineages: astrocytes, oligodendrocytes, and neurons. The latter are functional, mature cells displaying clear-cut morphological and physiological features of terminally differentiated neurons, encompassing mostly the GABAergic, glutamatergic, and cholinergic phenotypes. Finally, IhNSCs produce bona fide oligodendrocytes in fractions up to 20% of total cell number. This is in contrast to the negligible propensity of hNSCs to generate oligodendroglia reported so far. Thus, we describe an immortalized hNSC line endowed with the properties of normal hNSCs and suitable for developing the novel, reliable assays and reproducible high-throughput gene and drug screening that are essential in both diagnostics and cell therapy studies.

Identification of Multipotent Stem/Progenitor Cells in Murine Sclera

PubMed Central

Tsai, Chia-Ling; Wu, Pei-Chang; Fini, M. Elizabeth; Shi, Songtao

2011-01-01

Purpose. The sclera forms the fibrous outer coat of the eyeball and acts as a supportive framework. The purpose of this study was to examine whether the sclera contains mesenchymal stem/progenitor cells. Method. Scleral tissue from C57BL6/J mice was separated from the retina and choroid and subsequently enzyme digested to release single cells. Proliferation capacity, self-renewal capacity, and ability for multipotent differentiation were analyzed by BrdU labeling, flow cytometry, reverse transcriptase–polymerase chain reaction, immunocytochemistry, and in vivo transplantation. Results. The scleral stem/progenitor cells (SSPCs) possessed clonogenic and high doubling capacities. These cells were positive for the mesenchymal markers Sca-1, CD90.2, CD44, CD105, and CD73 and negative for the hematopoietic markers CD45, CD11b, Flk1, CD34, and CD117. In addition to expressing stem cell genes ABCG2, Six2, Notch1, and Pax6, SSPCs were able to differentiate to adipogenic, chondrogenic, and neurogenic lineages. Conclusions. This study indicates that the sclera contains multipotent mesenchymal stem cells. Further study of SSPCs may help elucidate the cellular and molecular mechanism of scleral diseases such as scleritis and myopia. PMID:21788434

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.

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.

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.

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.

Neural-Network Control Of Prosthetic And Robotic Hands

NASA Technical Reports Server (NTRS)

Buckley, Theresa M.

1991-01-01

Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

DTIC Science & Technology

1990-06-01

NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

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…

Histone Methylation and microRNA-dependent Regulation of Epigenetic Activities in Neural Progenitor Self-Renewal and Differentiation.

PubMed

Cacci, Emanuele; Negri, Rodolfo; Biagioni, Stefano; Lupo, Giuseppe

2017-01-01

Neural stem/progenitor cell (NSPC) self-renewal and differentiation in the developing and the adult brain are controlled by extra-cellular signals and by the inherent competence of NSPCs to produce appropriate responses. Stage-dependent responsiveness of NSPCs to extrinsic cues is orchestrated at the epigenetic level. Epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNA-mediated regulation control crucial aspects of NSPC development and function, and are also implicated in pathological conditions. While their roles in the regulation of stem cell fate have been largely explored in pluripotent stem cell models, the epigenetic signature of NSPCs is also key to determine their multipotency as well as their progressive bias towards specific differentiation outcomes. Here we review recent developments in this field, focusing on the roles of histone methylation marks and the protein complexes controlling their deposition in NSPCs of the developing cerebral cortex and the adult subventricular zone. In this context, we describe how bivalent promoters, carrying antagonistic epigenetic modifications, feature during multiple steps of neural development, from neural lineage specification to neuronal differentiation. Furthermore, we discuss the emerging cross-talk between epigenetic regulators and microRNAs, and how the interplay between these different layers of regulation can finely tune the expression of genes controlling NSPC maintenance and differentiation. In particular, we highlight recent advances in the identification of astrocyte-enriched microRNAs and their function in cell fate choices of NSPCs differentiating towards glial lineages.

Neural Network Control of a Magnetically Suspended Rotor System

NASA Technical Reports Server (NTRS)

Choi, Benjamin; Brown, Gerald; Johnson, Dexter

1997-01-01

Abstract Magnetic bearings offer significant advantages because of their noncontact 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. This paper discusses the use of a neural network as a nonlinear controller that circumvents system nonlinearity. A neural network controller was well trained and successfully demonstrated on a small magnetic bearing rig. This work demonstrated the feasibility of using a neural network to control nonlinear magnetic bearings and systems with unknown dynamics.

Adaptive control using neural networks and approximate models.

PubMed

Narendra, K S; Mukhopadhyay, S

1997-01-01

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

Neural-tree call admission controller for ATM networks

NASA Astrophysics Data System (ADS)

Rughooputh, Harry C. S.

1999-03-01

Asynchronous Transfer Mode (ATM) has been recommended by ITU-T as the transport method for broadband integrated services digital networks. In high-speed ATM networks different types of multimedia traffic streams with widely varying traffic characteristics and Quality of Service (QoS) are asynchronously multiplexed on transmission links and switched without window flow control as found in X.25. In such an environment, a traffic control scheme is required to manage the required QoS of each class individually. To meet the QoS requirements, Bandwidth Allocation and Call Admission Control (CAC) in ATM networks must be able to adapt gracefully to the dynamic behavior of traffic and the time-varying nature of the network condition. In this paper, a Neural Network approach for CAC is proposed. The call admission problem is addressed by designing controllers based on Neural Tree Networks. Simulations reveal that the proposed scheme is not only simple but it also offers faster response than conventional neural/neuro-fuzzy controllers.

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

PubMed

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

2015-01-01

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

Neural networks applications to control and computations

NASA Technical Reports Server (NTRS)

Luxemburg, Leon A.

1994-01-01

Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

DOT National Transportation Integrated Search

1996-01-01

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

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.

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

Nonlinear adaptive inverse control via the unified model neural network

NASA Astrophysics Data System (ADS)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Hexacopter trajectory control using a neural network

NASA Astrophysics Data System (ADS)

Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

2013-10-01

The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

Cell delamination in the mesencephalic neural fold and its implication for the origin of ectomesenchyme

PubMed Central

Lee, Raymond Teck Ho; Nagai, Hiroki; Nakaya, Yukiko; Sheng, Guojun; Trainor, Paul A.; Weston, James A.; Thiery, Jean Paul

2013-01-01

The neural crest is a transient structure unique to vertebrate embryos that gives rise to multiple lineages along the rostrocaudal axis. In cranial regions, neural crest cells are thought to differentiate into chondrocytes, osteocytes, pericytes and stromal cells, which are collectively termed ectomesenchyme derivatives, as well as pigment and neuronal derivatives. There is still no consensus as to whether the neural crest can be classified as a homogenous multipotent population of cells. This unresolved controversy has important implications for the formation of ectomesenchyme and for confirmation of whether the neural fold is compartmentalized into distinct domains, each with a different repertoire of derivatives. Here we report in mouse and chicken that cells in the neural fold delaminate over an extended period from different regions of the cranial neural fold to give rise to cells with distinct fates. Importantly, cells that give rise to ectomesenchyme undergo epithelial-mesenchymal transition from a lateral neural fold domain that does not express definitive neural markers, such as Sox1 and N-cadherin. Additionally, the inference that cells originating from the cranial neural ectoderm have a common origin and cell fate with trunk neural crest cells prompted us to revisit the issue of what defines the neural crest and the origin of the ectomesenchyme. PMID:24198279

Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

PubMed

Zhang, Junfeng; Zhao, Xudong; Huang, Jun

2016-11-01

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

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

Neural systems for preparatory control of imitation.

PubMed

Cross, Katy A; Iacoboni, Marco

2014-01-01

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

Feasibility study of robotic neural controllers

NASA Technical Reports Server (NTRS)

Magana, Mario E.

1990-01-01

The results are given of a feasibility study performed to establish if an artificial neural controller could be used to achieve joint space trajectory tracking of a two-link robot manipulator. The study is based on the results obtained by Hecht-Nielsen, who claims that a functional map can be implemented to a desired degree of accuracy with a three layer feedforward artificial neural network. Central to this study is the assumption that the robot model as well as its parameters values are known.

Neural self-tuning adaptive control of non-minimum phase system

NASA Technical Reports Server (NTRS)

Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Modeling and control of magnetorheological fluid dampers using neural networks

NASA Astrophysics Data System (ADS)

Wang, D. H.; Liao, W. H.

2005-02-01

Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

Neural networks for feedback feedforward nonlinear control systems.

PubMed

Parisini, T; Zoppoli, R

1994-01-01

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

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.

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.

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.

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.

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

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

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.

Neural autonomic control in orthostatic intolerance.

PubMed

Furlan, Raffaello; Barbic, Franca; Casella, Francesco; Severgnini, Giorgio; Zenoni, Luca; Mercieri, Angelo; Mangili, Ruggero; Costantino, Giorgio; Porta, Alberto

2009-10-01

Inability to maintain the upright position is manifested by a number of symptoms shared by either human pathophysiology and conditions following weightlessness or bed rest. Alterations of the neural sympathetic cardiovascular control have been suggested to be one of the potential underlying etiopathogenetic mechanisms in these conditions. We hypothesize that the study of the autonomic profile of human orthostatic intolerance syndromes may furnish a valuable insight into the complexity of the sympathetic alterations leading to a reduced gravitational tolerance. In the present paper we describe abnormalities both in the magnitude and in the pattern of the sympathetic neural firing observed in patients affected by orthostatic intolerance, attending the upright position. Also, we discuss similarity and differences in the neural sympathetic mechanisms regulating the cardiovascular system during the gravitational stimulus both in clinical syndromes and in subjects returning from space.

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.

Emergence and migration of trunk neural crest cells in a snake, the California Kingsnake (Lampropeltis getula californiae)

PubMed Central

2010-01-01

Background The neural crest is a group of multipotent cells that emerges after an epithelial-to-mesenchymal transition from the dorsal neural tube early during development. These cells then migrate throughout the embryo, giving rise to a wide variety derivatives including the peripheral nervous system, craniofacial skeleton, pigment cells, and endocrine organs. While much is known about neural crest cells in mammals, birds, amphibians and fish, relatively little is known about their development in non-avian reptiles like snakes and lizards. Results In this study, we show for the first time ever trunk neural crest migration in a snake by labeling it with DiI and immunofluorescence. As in birds and mammals, we find that early migrating trunk neural crest cells use both a ventromedial pathway and an inter-somitic pathway in the snake. However, unlike birds and mammals, we also observed large numbers of late migrating neural crest cells utilizing the inter-somitic pathway in snake. Conclusions We found that while trunk neural crest migration in snakes is very similar to that of other amniotes, the inter-somitic pathway is used more extensively by late-migrating trunk neural crest cells in snake. PMID:20482793

Emergence and migration of trunk neural crest cells in a snake, the California Kingsnake (Lampropeltis getula californiae).

PubMed

Reyes, Michelle; Zandberg, Katrina; Desmawati, Iska; de Bellard, Maria E

2010-05-18

The neural crest is a group of multipotent cells that emerges after an epithelial-to-mesenchymal transition from the dorsal neural tube early during development. These cells then migrate throughout the embryo, giving rise to a wide variety derivatives including the peripheral nervous system, craniofacial skeleton, pigment cells, and endocrine organs. While much is known about neural crest cells in mammals, birds, amphibians and fish, relatively little is known about their development in non-avian reptiles like snakes and lizards. In this study, we show for the first time ever trunk neural crest migration in a snake by labeling it with DiI and immunofluorescence. As in birds and mammals, we find that early migrating trunk neural crest cells use both a ventromedial pathway and an inter-somitic pathway in the snake. However, unlike birds and mammals, we also observed large numbers of late migrating neural crest cells utilizing the inter-somitic pathway in snake. We found that while trunk neural crest migration in snakes is very similar to that of other amniotes, the inter-somitic pathway is used more extensively by late-migrating trunk neural crest cells in snake.

Cultural influences on neural basis of inhibitory control.

PubMed

Pornpattananangkul, Narun; Hariri, Ahmad R; Harada, Tokiko; Mano, Yoko; Komeda, Hidetsugu; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya; Chiao, Joan Y

2016-10-01

Research on neural basis of inhibitory control has been extensively conducted in various parts of the world. It is often implicitly assumed that neural basis of inhibitory control is universally similar across cultures. Here, we investigated the extent to which culture modulated inhibitory-control brain activity at both cultural-group and cultural-value levels of analysis. During fMRI scanning, participants from different cultural groups (including Caucasian-Americans and Japanese-Americans living in the United States and native Japanese living in Japan) performed a Go/No-Go task. They also completed behavioral surveys assessing cultural values of behavioral consistency, or the extent to which one's behaviors in daily life are consistent across situations. Across participants, the Go/No-Go task elicited stronger neural activity in several inhibitory-control areas, such as the inferior frontal gyrus (IFG) and anterior cingulate cortex (ACC). Importantly, at the cultural-group level, we found variation in left IFG (L-IFG) activity that was explained by a cultural region where participants lived in (as opposed to race). Specifically, L-IFG activity was stronger for native Japanese compared to Caucasian- and Japanese-Americans, while there was no systematic difference in L-IFG activity between Japanese- and Caucasian-Americans. At the cultural-value level, we found that participants who valued being "themselves" across situations (i.e., having high endorsement of behavioral consistency) elicited stronger rostral ACC activity during the Go/No-Go task. Altogether, our findings provide novel insight into how culture modulates the neural basis of inhibitory control. Copyright © 2016 Elsevier Inc. All rights reserved.

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

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.

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

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.

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.

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.

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

Newly developed double neural network concept for reliable fast plasma position control

NASA Astrophysics Data System (ADS)

Jeon, Young-Mu; Na, Yong-Su; Kim, Myung-Rak; Hwang, Y. S.

2001-01-01

Neural network is considered as a parameter estimation tool in plasma controls for next generation tokamak such as ITER. The neural network has been reported to be so accurate and fast for plasma equilibrium identification that it may be applied to the control of complex tokamak plasmas. For this application, the reliability of the conventional neural network needs to be improved. In this study, a new idea of double neural network is developed to achieve this. The new idea has been applied to simple plasma position identification of KSTAR tokamak for feasibility test. Characteristics of the concept show higher reliability and fault tolerance even in severe faulty conditions, which may make neural network applicable to plasma control reliably and widely in future tokamaks.

Neural control of salivary glands in ixodid ticks.

PubMed

Šimo, Ladislav; Zitňan, Dušan; Park, Yoonseong

2012-04-01

Studies of tick salivary glands (SGs) and their components have produced a number of interesting discoveries over the last four decades. However, the precise neural and physiological mechanisms controlling SG secretion remain enigmatic. Major studies of SG control have identified and characterized many pharmacological and biological compounds that activate salivary secretion, including dopamine (DA), octopamine, γ-aminobutyric acid (GABA), ergot alkaloids, pilocarpine (PC), and their pharmacological relatives. Specifically, DA has shown the most robust activities in various tick species, and its effect on downstream actions in the SGs has been extensively studied. Our recent work on a SG dopamine receptor has aided new interpretations of previous pharmacological studies and provided new concepts for SG control mechanisms. Furthermore, our recent studies have suggested that multiple neuropeptides are involved in SG control. Myoinhibitory peptide (MIP) and SIFamide have been identified in the neural projections reaching the basal cells of acini types II and III. Pigment-dispersing factor (PDF)-immunoreactive neural projections reach type II acini, and RFamide- and tachykinin-immunoreactive projections reach the SG ducts, but the chemical nature of the latter three immunoreactive substances are unidentified yet. Here, we briefly review previous pharmacological studies and provide a revised summary of SG control mechanisms in ticks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Proliferation of multipotent hematopoietic cells controlled by a truncated erythropoietin receptor transgene.

PubMed Central

Kirby, S L; Cook, D N; Walton, W; Smithies, O

1996-01-01

The long-term efficacy of gene therapy using bone marrow transplantation requires the engraftment of genetically altered totipotent hematopoietic stem cells (THSCs). Ex vivo expansion of corrected THSCs is one way to increase the efficiency of the procedure. Similarly, selective in vivo expansion of the therapeutic THSCs rather than the endogenous THSCs could favor the transplant. To test whether a conferred proliferative advantage gene can facilitate the in vitro and in vivo expansion of hematopoietic stem cells, we have generated transgenic mice expressing a truncated receptor for the growth factor erythropoietin. These mice are phenotypically normal, but when treated in vivo with exogenous erythropoietin they exhibit a marked increase in multipotent, clonogenic hematopoietic cells [colony-forming units in the spleen (CFU-S) and CFUs that give rise to granulocytes, erythroid cells, macrophages, and megakaryocytes within the same colony (CFU-GEMM)] in comparison with the wild-type mice. In addition, long-term in vitro culture of tEpoR transgenic bone marrow in the presence of erythropoietin induces exponential expansion of trilineage hematopoietic stem cells not seen with wild-type bone marrow. Thus, the truncated erythropoietin receptor gene shows promise as a means for obtaining cytokine-inducible hematopoietic stem cell proliferation to facilitate the direct targeting of THSCs and to provide a competitive repopulation advantage for transplanted therapeutic stem cells. Images Fig. 3 PMID:8790342

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.

Characterization of Pax3 and Sox10 transgenic Xenopus laevis embryos as tools to study neural crest development.

PubMed

Alkobtawi, Mansour; Ray, Heather; Barriga, Elias H; Moreno, Mauricio; Kerney, Ryan; Monsoro-Burq, Anne-Helene; Saint-Jeannet, Jean-Pierre; Mayor, Roberto

2018-03-06

The neural crest is a multipotent population of cells that originates a variety of cell types. Many animal models are used to study neural crest induction, migration and differentiation, with amphibians and birds being the most widely used systems. A major technological advance to study neural crest development in mouse, chick and zebrafish has been the generation of transgenic animals in which neural crest specific enhancers/promoters drive the expression of either fluorescent proteins for use as lineage tracers, or modified genes for use in functional studies. Unfortunately, no such transgenic animals currently exist for the amphibians Xenopus laevis and tropicalis, key model systems for studying neural crest development. Here we describe the generation and characterization of two transgenic Xenopus laevis lines, Pax3-GFP and Sox10-GFP, in which GFP is expressed in the pre-migratory and migratory neural crest, respectively. We show that Pax3-GFP could be a powerful tool to study neural crest induction, whereas Sox10-GFP could be used in the study of neural crest migration in living embryos. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Clonally expanded novel multipotent stem cells from human bone marrow regenerate myocardium after myocardial infarction

PubMed Central

Yoon, Young-sup; Wecker, Andrea; Heyd, Lindsay; Park, Jong-Seon; Tkebuchava, Tengiz; Kusano, Kengo; Hanley, Allison; Scadova, Heather; Qin, Gangjian; Cha, Dong-Hyun; Johnson, Kirby L.; Aikawa, Ryuichi; Asahara, Takayuki; Losordo, Douglas W.

2005-01-01

We have identified a subpopulation of stem cells within adult human BM, isolated at the single-cell level, that self-renew without loss of multipotency for more than 140 population doublings and exhibit the capacity for differentiation into cells of all 3 germ layers. Based on surface marker expression, these clonally expanded human BM-derived multipotent stem cells (hBMSCs) do not appear to belong to any previously described BM-derived stem cell population. Intramyocardial transplantation of hBMSCs after myocardial infarction resulted in robust engraftment of transplanted cells, which exhibited colocalization with markers of cardiomyocyte (CMC), EC, and smooth muscle cell (SMC) identity, consistent with differentiation of hBMSCs into multiple lineages in vivo. Furthermore, upregulation of paracrine factors including angiogenic cytokines and antiapoptotic factors, and proliferation of host ECs and CMCs, were observed in the hBMSC-transplanted hearts. Coculture of hBMSCs with CMCs, ECs, or SMCs revealed that phenotypic changes of hBMSCs result from both differentiation and fusion. Collectively, the favorable effect of hBMSC transplantation after myocardial infarction appears to be due to augmentation of proliferation and preservation of host myocardial tissues as well as differentiation of hBMSCs for tissue regeneration and repair. To our knowledge, this is the first demonstration that a specific population of multipotent human BM-derived stem cells can induce both therapeutic neovascularization and endogenous and exogenous cardiomyogenesis. PMID:15690083

Three Pillars for the Neural Control of Appetite.

PubMed

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Single-Factor SOX2 Mediates Direct Neural Reprogramming of Human Mesenchymal Stem Cells via Transfection of In Vitro Transcribed mRNA.

PubMed

Kim, Bo-Eun; Choi, Soon Won; Shin, Ji-Hee; Kim, Jae-Jun; Kang, Insung; Lee, Byung-Chul; Lee, Jin Young; Kook, Myoung Geun; Kang, Kyung-Sun

2018-01-01

Neural stem cells (NSCs) are a prominent cell source for understanding neural pathogenesis and for developing therapeutic applications to treat neurodegenerative disease because of their regenerative capacity and multipotency. Recently, a variety of cellular reprogramming technologies have been developed to facilitate in vitro generation of NSCs, called induced NSCs (iNSCs). However, the genetic safety aspects of established virus-based reprogramming methods have been considered, and non-integrating reprogramming methods have been developed. Reprogramming with in vitro transcribed (IVT) mRNA is one of the genetically safe reprogramming methods because exogenous mRNA temporally exists in the cell and is not integrated into the chromosome. Here, we successfully generated expandable iNSCs from human umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs) via transfection with IVT mRNA encoding SOX2 (SOX2 mRNA) with properly optimized conditions. We confirmed that generated human UCB-MSC-derived iNSCs (UM-iNSCs) possess characteristics of NSCs, including multipotency and self-renewal capacity. Additionally, we transfected human dermal fibroblasts (HDFs) with SOX2 mRNA. Compared with human embryonic stem cell-derived NSCs, HDFs transfected with SOX2 mRNA exhibited neural reprogramming with similar morphologies and NSC-enriched mRNA levels, but they showed limited proliferation ability. Our results demonstrated that human UCB-MSCs can be used for direct reprogramming into NSCs through transfection with IVT mRNA encoding a single factor, which provides an integration-free reprogramming tool for future therapeutic application.

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

NASA Astrophysics Data System (ADS)

Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

2018-02-01

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

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.

Should I stay or should I go? Cadherin function and regulation in the neural crest

PubMed Central

Taneyhill, Lisa A.; Schiffmacher, Andrew T.

2017-01-01

Our increasing comprehension of neural crest cell development has reciprocally advanced our understanding of cadherin expression, regulation, and function. As a transient population of multipotent stem cells that significantly contribute to the vertebrate body plan, neural crest cells undergo a variety of transformative processes and exhibit many cellular behaviors, including epithelial-to-mesenchymal-transition (EMT), motility, collective cell migration, and differentiation. Multiple studies have elucidated regulatory and mechanistic details of specific cadherins during neural crest cell development in a highly contextual manner. Collectively, these results reveal that gradual changes within neural crest cells are accompanied by often times subtle, yet important, alterations in cadherin expression and function. The primary focus of this review is to coalesce recent data on cadherins in neural crest cells, from their specification to their emergence as motile cells soon after EMT, and to highlight the complexities of cadherin expression beyond our current perceptions, including the hypothesis that the neural crest EMT is a transition involving a predominantly singular cadherin switch. Further advancements in genetic approaches and molecular techniques will provide greater opportunities to integrate data from various model systems in order to distinguish unique or overlapping functions of cadherins expressed at any point throughout the ontogeny of the neural crest. PMID:28253541

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

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.

Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

PubMed

Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

2017-10-01

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

PubMed

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Neural dynamic optimization for control systems. I. Background.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Neural dynamic optimization for control systems.III. Applications.

PubMed

Seong, C Y; Widrow, B

2001-01-01

For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively.

Neural dynamic optimization for control systems.II. Theory.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Plasma membrane characterization, by scanning electron microscopy, of multipotent myoblasts-derived populations sorted using dielectrophoresis.

PubMed

Muratore, Massimo; Mitchell, Steve; Waterfall, Martin

2013-09-06

Multipotent progenitor cells have shown promise for use in biomedical applications and regenerative medicine. The implementation of such cells for clinical application requires a synchronized, phenotypically and/or genotypically, homogenous cell population. Here we have demonstrated the implementation of a biological tag-free dielectrophoretic device used for discrimination of multipotent myoblastic C2C12 model. The multipotent capabilities in differentiation, for these cells, diminishes with higher passage number, so for cultures above 70 passages only a small percentage of cells is able to differentiate into terminal myotubes. In this work we demonstrated that we could recover, above 96% purity, specific cell types from a mixed population of cells at high passage number without any biological tag using dielectrophoresis. The purity of the samples was confirmed by cytometric analysis using the cell specific marker embryonic myosin. To further investigate the dielectric properties of the cell plasma membrane we co-culture C2C12 with similar size, when in suspension, GFP-positive fibroblast as feeder layer. The level of separation between the cell types was above 98% purity which was confirmed by flow cytometry. These levels of separation are assumed to account for cell size and for the plasma membrane morphological differences between C2C12 and fibroblast unrelated to the stages of the cell cycle which was assessed by immunofluorescence staining. Plasma membrane conformational differences were further confirmed by scanning electron microscopy. Copyright © 2013 Elsevier Inc. All rights reserved.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

PubMed

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

PubMed Central

Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

2010-01-01

SUMMARY The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identified as substrates for ADAM13. ADAM13 upregulates canonical Wnt signaling and early expression of the transcription factor snail2, whereas EfnB1 inhibits the canonical Wnt pathway and snail2 expression. We propose that by cleaving class B Efns, ADAM13 promotes canonical Wnt signaling and early CNC induction. PMID:20708595

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.

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

Basal Cells Are a Multipotent Progenitor Capable of Renewing the Bronchial Epithelium

PubMed Central

Hong, Kyung U.; Reynolds, Susan D.; Watkins, Simon; Fuchs, Elaine; Stripp, Barry R.

2004-01-01

Commitment of the pulmonary epithelium to bronchial and bronchiolar airway lineages occurs during the transition from pseudoglandular to cannalicular phases of lung development, suggesting that regional differences exist with respect to the identity of stem and progenitor cells that contribute to epithelial maintenance in adulthood. We previously defined a critical role for Clara cell secretory protein-expressing (CE) cells in renewal of bronchiolar airway epithelium following injury. Even though CE cells are also the principal progenitor for maintenance of the bronchial airway epithelium, CE cell injury is resolved through a mechanism involving recruitment of a second progenitor cell population that we now identify as a GSI-B4 reactive, cytokeratin-14-expressing basal cell. These cells exhibit multipotent differentiation capacity as assessed by analysis of cellular phenotype within clones of LacZ-tagged cells. Clones were derived from K14-expressing cells tagged in a cell-type-specific fashion by ligand-regulable Cre recombinase-mediated genomic rearrangement of the ROSA26 recombination substrate allele. We conclude that basal cells represent an alternative multipotent progenitor cell population of bronchial airways and that progenitor cell selection is dictated by the type of airway injury. PMID:14742263

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Path optimisation of a mobile robot using an artificial neural network controller

NASA Astrophysics Data System (ADS)

Singh, M. K.; Parhi, D. R.

2011-01-01

This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.

Neural network feedforward control of a closed-circuit wind tunnel

NASA Astrophysics Data System (ADS)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

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.

Neurodegeneration and adaptation in response to low-dose photon irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Limoli, Charles L.

2014-10-27

Neural stem and precursor cells (i.e. multipotent neural cells) are concentrated in the neurogenic regions of the brain (hippocampal dentate gyrus, subventricular zones), and considerable evidence suggests that these cells are important in mediating the stress response of the CNS after damage from ionizing radiation. The capability of these cells to proliferate, migrate and differentiate (i.e. to undergo neurogenesis) suggests they can participate in the repair and maintenance of CNS functions by replacing brain cells damaged or depleted due to irradiation. Importantly, we have shown that multipotent neural cells are markedly sensitive to irradiation and oxidative stress, insults that compromisemore » neurogenesis and hasten the onset and progression of degenerative processes that are likely to have an adverse impact on cognition. Our past and current work has demonstrated that relatively low doses of radiation cause a persistent (weeks-months) oxidative stress in multipotent neural cells that can elicit a range of degenerative sequelae in the CNS. Therefore, our project is focused on determining the extent that endogenous and redox sensitive multipotent neural cells represent important radioresponsive targets for low dose radiation effects. We hypothesize that the activation of redox sensitive signaling can trigger radioadaptive changes in these cells that can be either harmful or beneficial to overall cognitive health.« less

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…

Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

PubMed

Chen, Ziting; Li, Zhijun; Chen, C L Philip

2017-06-01

An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

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.

IL-7Rα and E47: independent pathways required for development of multipotent lymphoid progenitors

PubMed Central

Kee, Barbara L.; Bain, Gretchen; Murre, Cornelis

2002-01-01

Mice that lack the transcription factors encoded by the E2A gene or the receptor for interleukin 7 (IL-7R) have severe overlapping defects in lymphocyte development. Here, we show that E2A proteins are required for the survival of early T-lineage cells; however, they function through a pathway that is distinct from the survival pathway initiated by IL-7R signaling. While E2A proteins are required to suppress caspase 3 activation, ectopic expression of the anti-apoptotic protein Bcl-2 is not sufficient to overcome the lymphopoietic defects observed in the absence of E2A. Remarkably, mice that lack both IL-7Rα and E47 display a synergistic decrease in the number of T-cell, NK-cell and multipotent progenitors in the thymus, indicating that these distinct survival pathways converge to promote the development of multipotent lymphoid progenitors. PMID:11782430

Region stability analysis and tracking control of memristive recurrent neural network.

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 modelmore » 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.« less

Variable Neural Adaptive Robust Control: A Switched System Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Masri Husam Fayiz, Al

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

A Voltage-Sensitive Dye-Based Assay for the Identification of Differentiated Neurons Derived from Embryonic Neural Stem Cell Cultures

PubMed Central

Emirandetti, Amanda; Lewicka, Michalina; Hermanson, Ola; Fisahn, André

2010-01-01

Background Pluripotent and multipotent stem cells hold great therapeutical promise for the replacement of degenerated tissue in neurological diseases. To fulfill that promise we have to understand the mechanisms underlying the differentiation of multipotent cells into specific types of neurons. Embryonic stem cell (ESC) and embryonic neural stem cell (NSC) cultures provide a valuable tool to study the processes of neural differentiation, which can be assessed using immunohistochemistry, gene expression, Ca2+-imaging or electrophysiology. However, indirect methods such as protein and gene analysis cannot provide direct evidence of neuronal functionality. In contrast, direct methods such as electrophysiological techniques are well suited to produce direct evidence of neural functionality but are limited to the study of a few cells on a culture plate. Methodology/Principal Findings In this study we describe a novel method for the detection of action potential-capable neurons differentiated from embryonic NSC cultures using fast voltage-sensitive dyes (VSD). We found that the use of extracellularly applied VSD resulted in a more detailed labeling of cellular processes compared to calcium indicators. In addition, VSD changes in fluorescence translated precisely to action potential kinetics as assessed by the injection of simulated slow and fast sodium currents using the dynamic clamp technique. We further demonstrate the use of a finite element model of the NSC culture cover slip for optimizing electrical stimulation parameters. Conclusions/Significance Our method allows for a repeatable fast and accurate stimulation of neurons derived from stem cell cultures to assess their differentiation state, which is capable of monitoring large amounts of cells without harming the overall culture. PMID:21079795

A neural network controller of a flotation process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Durao, F.; Cortez, L.

1995-12-31

The dynamic control of a froth flotation section is simulated through a neural network feedback controller, trained in order to stabilize the concentrate metal grade and recovery by applying random step changes to the feed metal grade. The results of the application example show that this controller seems to be sufficiently robust and a good alternative to handle a non-linear process.

Generation of Regionally Specific Neural Progenitor Cells (NPCs) and Neurons from Human Pluripotent Stem Cells (hPSCs).

PubMed

Cutts, Josh; Brookhouser, Nicholas; Brafman, David A

2016-01-01

Neural progenitor cells (NPCs) derived from human pluripotent stem cells (hPSCs) are a multipotent cell population capable of long-term expansion and differentiation into a variety of neuronal subtypes. As such, NPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine. Current methods for the generation of NPCs results in cell populations homogenous for pan-neural markers such as SOX1 and SOX2 but heterogeneous with respect to regional identity. In order to use NPCs and their neuronal derivatives to investigate mechanisms of neurological disorders and develop more physiologically relevant disease models, methods for generation of regionally specific NPCs and neurons are needed. Here, we describe a protocol in which exogenous manipulation of WNT signaling, through either activation or inhibition, during neural differentiation of hPSCs, promotes the formation of regionally homogenous NPCs and neuronal cultures. In addition, we provide methods to monitor and characterize the efficiency of hPSC differentiation to these regionally specific cell identities.

Adult multipotent stromal cell cryopreservation: Pluses and pitfalls

PubMed Central

Duan, Wei; Hicok, Kevin

2017-01-01

Abstract Study and clinical testing of adult multipotent stromal cells (MSCs) are central to progressive improvements in veterinary regenerative medicine. Inherent limitations to long‐term culture preclude use for storage. Until cell line creation from primary isolates becomes routine, MSC stasis at cryogenic temperatures is required for this purpose. Many protocols and reagents, including cryoprotectants, used for veterinary MSCs are derived from those for human and rodent cells. Dissimilarities in cryopreservation strategies play a role in variable MSC behaviors. Familiarity with contemporary cryopreservation reagents and processes is essential to an appreciation of their impact on MSC survival and post‐cryopreservation behavior. In addition to these points, this review includes a brief history and description of current veterinary stem cell regulation. PMID:29023790

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.

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.

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

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

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

Cyclin-dependent kinase 4 signaling acts as a molecular switch between syngenic differentiation and neural transdifferentiation in human mesenchymal stem cells

PubMed Central

Lee, Janet; Baek, Jeong-Hwa; Choi, Kyu-Sil; Kim, Hyun-Soo; Park, Hye-Young; Ha, Geun-Hyoung; Park, Ho; Lee, Kyo-Won; Lee, Chang Geun; Yang, Dong-Yun; Moon, Hyo Eun; Paek, Sun Ha; Lee, Chang-Woo

2013-01-01

Multipotent mesenchymal stem/stromal cells (MSCs) are capable of differentiating into a variety of cell types from different germ layers. However, the molecular and biochemical mechanisms underlying the transdifferentiation of MSCs into specific cell types still need to be elucidated. In this study, we unexpectedly found that treatment of human adipose- and bone marrow-derived MSCs with cyclin-dependent kinase (CDK) inhibitor, in particular CDK4 inhibitor, selectively led to transdifferentiation into neural cells with a high frequency. Specifically, targeted inhibition of CDK4 expression using recombinant adenovial shRNA induced the neural transdifferentiation of human MSCs. However, the inhibition of CDK4 activity attenuated the syngenic differentiation of human adipose-derived MSCs. Importantly, the forced regulation of CDK4 activity showed reciprocal reversibility between neural differentiation and dedifferentiation of human MSCs. Together, these results provide novel molecular evidence underlying the neural transdifferentiation of human MSCs; in addition, CDK4 signaling appears to act as a molecular switch from syngenic differentiation to neural transdifferentiation of human MSCs. PMID:23324348

Generation of a human airway epithelium derived basal cell line with multipotent differentiation capacity

PubMed Central

2013-01-01

Background As the multipotent progenitor population of the airway epithelium, human airway basal cells (BC) replenish the specialized differentiated cell populations of the mucociliated airway epithelium during physiological turnover and repair. Cultured primary BC divide a limited number of times before entering a state of replicative senescence, preventing the establishment of long-term replicating cultures of airway BC that maintain their original phenotype. Methods To generate an immortalized human airway BC cell line, primary human airway BC obtained by brushing the airway epithelium of healthy nonsmokers were infected with a retrovirus expressing human telomerase (hTERT). The resulting immortalized cell line was then characterized under non-differentiating and differentiating air-liquid interface (ALI) culture conditions using ELISA, TaqMan quantitative PCR, Western analysis, and immunofluorescent and immunohistochemical staining analysis for cell type specific markers. In addition, the ability of the cell line to respond to environmental stimuli under differentiating ALI culture was assessed. Results We successfully generated an immortalized human airway BC cell line termed BCi-NS1 via expression of hTERT. A single cell derived clone from the parental BCi-NS1 cells, BCi-NS1.1, retains characteristics of the original primary cells for over 40 passages and demonstrates a multipotent differentiation capacity into secretory (MUC5AC, MUC5B), goblet (TFF3), Clara (CC10) and ciliated (DNAI1, FOXJ1) cells on ALI culture. The cells can respond to external stimuli such as IL-13, resulting in alteration of the normal differentiation process. Conclusion Development of immortalized human airway BC that retain multipotent differentiation capacity over long-term culture should be useful in understanding the biology of BC, the response of BC to environmental stress, and as a target for assessment of pharmacologic agents. PMID:24298994

Topological defects control collective dynamics in neural progenitor cell cultures

NASA Astrophysics Data System (ADS)

Kawaguchi, Kyogo; Kageyama, Ryoichiro; Sano, Masaki

2017-04-01

Cultured stem cells have become a standard platform not only for regenerative medicine and developmental biology but also for biophysical studies. Yet, the characterization of cultured stem cells at the level of morphology and of the macroscopic patterns resulting from cell-to-cell interactions remains largely qualitative. Here we report on the collective dynamics of cultured murine neural progenitor cells (NPCs), which are multipotent stem cells that give rise to cells in the central nervous system. At low densities, NPCs moved randomly in an amoeba-like fashion. However, NPCs at high density elongated and aligned their shapes with one another, gliding at relatively high velocities. Although the direction of motion of individual cells reversed stochastically along the axes of alignment, the cells were capable of forming an aligned pattern up to length scales similar to that of the migratory stream observed in the adult brain. The two-dimensional order of alignment within the culture showed a liquid-crystalline pattern containing interspersed topological defects with winding numbers of +1/2 and -1/2 (half-integer due to the nematic feature that arises from the head-tail symmetry of cell-to-cell interaction). We identified rapid cell accumulation at +1/2 defects and the formation of three-dimensional mounds. Imaging at the single-cell level around the defects allowed us to quantify the velocity field and the evolving cell density; cells not only concentrate at +1/2 defects, but also escape from -1/2 defects. We propose a generic mechanism for the instability in cell density around the defects that arises from the interplay between the anisotropic friction and the active force field.

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

PubMed

Klaver, Peter; Schnaidt, Malte; Fell, Jürgen; Ruhlmann, Jürgen; Elger, Christian E; Fernández, Guillén

2007-02-15

Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either pictures or words that were presented superimposed on each other. In the repetition phase only words were presented that were new, previously attended or ignored, or picture names that were derived from previously attended or ignored pictures. Relative to new words we found repetition priming for previously attended words. Previously ignored words showed a reduced priming effect, and there was no significant priming for pictures repeated as picture names. Brain imaging data showed that neural priming of words in the left prefrontal cortex (LIPFC) and left fusiform gyrus (LOTC) was affected by attention, semantic compatibility of superimposed stimuli during study and cross-modal priming. Neural priming reduced for words in the LIPFC and for words and pictures in the LOTC if stimuli were previously ignored. Previously ignored words that were semantically incompatible with a superimposed picture during study induce increased neural priming compared to semantically compatible ignored words (LIPFC) and decreased neural priming of previously attended pictures (LOTC). In summary, top-down control induces dissociable effects on neural priming by attention, cross-modal priming and semantic compatibility in a way that was not evident from behavioral results.

Distinct neural circuits for control of movement vs. holding still

PubMed Central

2017-01-01

In generating a point-to-point movement, the brain does more than produce the transient commands needed to move the body part; it also produces the sustained commands that are needed to hold the body part at its destination. In the oculomotor system, these functions are mapped onto two distinct circuits: a premotor circuit that specializes in generating the transient activity that displaces the eyes and a “neural integrator” that transforms that transient input into sustained activity that holds the eyes. Different parts of the cerebellum adaptively control the motor commands during these two phases: the oculomotor vermis participates in fine tuning the transient neural signals that move the eyes, monitoring the activity of the premotor circuit via efference copy, whereas the flocculus participates in controlling the sustained neural signals that hold the eyes, monitoring the activity of the neural integrator. Here, I review the oculomotor literature and then ask whether this separation of control between moving and holding is a design principle that may be shared with other modalities of movement. To answer this question, I consider neurophysiological and psychophysical data in various species during control of head movements, arm movements, and locomotion, focusing on the brain stem, motor cortex, and hippocampus, respectively. The review of the data raises the possibility that across modalities of motor control, circuits that are responsible for producing commands that change the sensory state of a body part are distinct from those that produce commands that maintain that sensory state. PMID:28053244

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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Neural control of rhythmic arm cycling after stroke

PubMed Central

Loadman, Pamela M.; Hundza, Sandra R.

2012-01-01

Disordered reflex activity and alterations in the neural control of walking have been observed after stroke. In addition to impairments in leg movement that affect locomotor ability after stroke, significant impairments are also seen in the arms. Altered neural control in the upper limb can often lead to altered tone and spasticity resulting in impaired coordination and flexion contractures. We sought to address the extent to which the neural control of movement is disordered after stroke by examining the modulation pattern of cutaneous reflexes in arm muscles during arm cycling. Twenty-five stroke participants who were at least 6 mo postinfarction and clinically stable, performed rhythmic arm cycling while cutaneous reflexes were evoked with trains (5 × 1.0-ms pulses at 300 Hz) of constant-current electrical stimulation to the superficial radial (SR) nerve at the wrist. Both the more (MA) and less affected (LA) arms were stimulated in separate trials. Bilateral electromyography (EMG) activity was recorded from muscles acting at the shoulder, elbow, and wrist. Analysis was conducted on averaged reflexes in 12 equidistant phases of the movement cycle. Phase-modulated cutaneous reflexes were present, but altered, in both MA and LA arms after stroke. Notably, the pattern was “blunted” in the MA arm in stroke compared with control participants. Differences between stroke and control were progressively more evident moving from shoulder to wrist. The results suggest that a reduced pattern of cutaneous reflex modulation persists during rhythmic arm movement after stroke. The overall implication of this result is that the putative spinal contributions to rhythmic human arm movement remain accessible after stroke, which has translational implications for rehabilitation. PMID:22572949

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.

Establishment of a Human Blood-Brain Barrier Co-culture Model Mimicking the Neurovascular Unit Using Induced Pluri- and Multipotent Stem Cells.

PubMed

Appelt-Menzel, Antje; Cubukova, Alevtina; Günther, Katharina; Edenhofer, Frank; Piontek, Jörg; Krause, Gerd; Stüber, Tanja; Walles, Heike; Neuhaus, Winfried; Metzger, Marco

2017-04-11

In vitro models of the human blood-brain barrier (BBB) are highly desirable for drug development. This study aims to analyze a set of ten different BBB culture models based on primary cells, human induced pluripotent stem cells (hiPSCs), and multipotent fetal neural stem cells (fNSCs). We systematically investigated the impact of astrocytes, pericytes, and NSCs on hiPSC-derived BBB endothelial cell function and gene expression. The quadruple culture models, based on these four cell types, achieved BBB characteristics including transendothelial electrical resistance (TEER) up to 2,500 Ω cm 2 and distinct upregulation of typical BBB genes. A complex in vivo-like tight junction (TJ) network was detected by freeze-fracture and transmission electron microscopy. Treatment with claudin-specific TJ modulators caused TEER decrease, confirming the relevant role of claudin subtypes for paracellular tightness. Drug permeability tests with reference substances were performed and confirmed the suitability of the models for drug transport studies. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Multipotent versus differentiated cell fate selection in the developing Drosophila airways

PubMed Central

Matsuda, Ryo; Hosono, Chie; Samakovlis, Christos; Saigo, Kaoru

2015-01-01

Developmental potentials of cells are tightly controlled at multiple levels. The embryonic Drosophila airway tree is roughly subdivided into two types of cells with distinct developmental potentials: a proximally located group of multipotent adult precursor cells (P-fate) and a distally located population of more differentiated cells (D-fate). We show that the GATA-family transcription factor (TF) Grain promotes the P-fate and the POU-homeobox TF Ventral veinless (Vvl/Drifter/U-turned) stimulates the D-fate. Hedgehog and receptor tyrosine kinase (RTK) signaling cooperate with Vvl to drive the D-fate at the expense of the P-fate while negative regulators of either of these signaling pathways ensure P-fate specification. Local concentrations of Decapentaplegic/BMP, Wingless/Wnt, and Hedgehog signals differentially regulate the expression of D-factors and P-factors to transform an equipotent primordial field into a concentric pattern of radially different morphogenetic potentials, which gradually gives rise to the distal-proximal organization of distinct cell types in the mature airway. DOI: http://dx.doi.org/10.7554/eLife.09646.001 PMID:26633813

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.

Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

PubMed

Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

2016-12-01

This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Income, neural executive processes, and preschool children's executive control.

PubMed

Ruberry, Erika J; Lengua, Liliana J; Crocker, Leanna Harris; Bruce, Jacqueline; Upshaw, Michaela B; Sommerville, Jessica A

2017-02-01

This study aimed to specify the neural mechanisms underlying the link between low household income and diminished executive control in the preschool period. Specifically, we examined whether individual differences in the neural processes associated with executive attention and inhibitory control accounted for income differences observed in performance on a neuropsychological battery of executive control tasks. The study utilized a sample of preschool-aged children (N = 118) whose families represented the full range of income, with 32% of families at/near poverty, 32% lower income, and 36% middle to upper income. Children completed a neuropsychological battery of executive control tasks and then completed two computerized executive control tasks while EEG data were collected. We predicted that differences in the event-related potential (ERP) correlates of executive attention and inhibitory control would account for income differences observed on the executive control battery. Income and ERP measures were related to performance on the executive control battery. However, income was unrelated to ERP measures. The findings suggest that income differences observed in executive control during the preschool period might relate to processes other than executive attention and inhibitory control.

Obesity-related differences in neural correlates of force control.

PubMed

Mehta, Ranjana K; Shortz, Ashley E

2014-01-01

Greater body segment mass due to obesity has shown to impair gross and fine motor functions and reduce balance control. While recent studies suggest that obesity may be linked with altered brain functions involved in fine motor tasks, this association is not well investigated. The purpose of this study was to examine the neural correlates of motor performance in non-obese and obese adults during force control of two upper extremity muscles. Nine non-obese and eight obese young adults performed intermittent handgrip and elbow flexion exertions at 30% of their respective muscle strengths for 4 min. Functional near infrared spectroscopy was employed to measure neural activity in the prefrontal cortex bilaterally, joint steadiness was computed using force fluctuations, and ratings of perceived exertions (RPEs) were obtained to assess perceived effort. Obesity was associated with higher force fluctuations and lower prefrontal cortex activation during handgrip exertions, while RPE scores remained similar across both groups. No obesity-related differences in neural activity, force fluctuation, or RPE scores were observed during elbow flexion exertions. The study is one of the first to examine obesity-related differences on prefrontal cortex activation during force control of the upper extremity musculature. The study findings indicate that the neural correlates of motor activity in the obese may be muscle-specific. Future work is warranted to extend the investigation to monitoring multiple motor-function related cortical regions and examining obesity differences with different task parameters (e.g., longer duration, increased precision demands, larger muscles, etc.).

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

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.

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. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A chemically defined substrate for the expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells.

PubMed

Tsai, Yihuan; Cutts, Josh; Kimura, Azuma; Varun, Divya; Brafman, David A

2015-07-01

Due to the limitation of current pharmacological therapeutic strategies, stem cell therapies have emerged as a viable option for treating many incurable neurological disorders. Specifically, human pluripotent stem cell (hPSC)-derived neural progenitor cells (hNPCs), a multipotent cell population that is capable of near indefinite expansion and subsequent differentiation into the various cell types that comprise the central nervous system (CNS), could provide an unlimited source of cells for such cell-based therapies. However the clinical application of these cells will require (i) defined, xeno-free conditions for their expansion and neuronal differentiation and (ii) scalable culture systems that enable their expansion and neuronal differentiation in numbers sufficient for regenerative medicine and drug screening purposes. Current extracellular matrix protein (ECMP)-based substrates for the culture of hNPCs are expensive, difficult to isolate, subject to batch-to-batch variations, and, therefore, unsuitable for clinical application of hNPCs. Using a high-throughput array-based screening approach, we identified a synthetic polymer, poly(4-vinyl phenol) (P4VP), that supported the long-term proliferation and self-renewal of hNPCs. The hNPCs cultured on P4VP maintained their characteristic morphology, expressed high levels of markers of multipotency, and retained their ability to differentiate into neurons. Such chemically defined substrates will eliminate critical roadblocks for the utilization of hNPCs for human neural regenerative repair, disease modeling, and drug discovery. Copyright © 2015. Published by Elsevier B.V.

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

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

PubMed

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

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

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).

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

PubMed

Long, Lijun; Zhao, Jun

2015-07-01

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

Isolation and culture of human multipotent stromal cells from the pancreas.

PubMed

Seeberger, Karen L; Eshpeter, Alana; Korbutt, Gregory S

2011-01-01

Mesenchymal stem cells, also termed multipotent mesenchymal stromal cells (MSCs), can be isolated from most adult tissues. Although the exact origin of MSCs expanded from the human pancreas has not been resolved, we have developed protocols to isolate and expand MSCs from human pancreatic tissue that remains after islet procurement. Similar to techniques used to isolate MSCs from bone marrow, pancreatic MSCs are isolated based on their cell adherence, expression of several cell surface antigens, and multilineage differentiation. The protocols for isolating, characterizing, and differentiating MSCs from the pancreas are presented in this chapter.

Dynamic methylation and expression of Oct4 in early neural stem cells

PubMed Central

Lee, Shih-Han; Jeyapalan, Jennie N; Appleby, Vanessa; Mohamed Noor, Dzul Azri; Sottile, Virginie; Scotting, Paul J

2010-01-01

Neural stem cells are a multipotent population of tissue-specific stem cells with a broad but limited differentiation potential. However, recent studies have shown that over-expression of the pluripotency gene, Oct4, alone is sufficient to initiate a process by which these can form ‘induced pluripotent stem cells’ (iPS cells) with the same broad potential as embryonic stem cells. This led us to examine the expression of Oct4 in endogenous neural stem cells, as data regarding its expression in neural stem cells in vivo are contradictory and incomplete. In this study we have therefore analysed the expression of Oct4 and other genes associated with pluripotency throughout development of the mouse CNS and in neural stem cells grown in vitro. We find that Oct4 is still expressed in the CNS by E8.5, but that this expression declines rapidly until it is undetectable by E15.5. This decline is coincident with the gradual methylation of the Oct4 promoter and proximal enhancer. Immunostaining suggests that the Oct4 protein is predominantly cytoplasmic in location. We also found that neural stem cells from all ages expressed the pluripotency associated genes, Sox2, c-Myc, Klf4 and Nanog. These data provide an explanation for the varying behaviour of cells from the early neuroepithelium at different stages of development. The expression of these genes also provides an indication of why Oct4 alone is sufficient to induce iPS formation in neural stem cells at later stages. PMID:20646110

Dynamic methylation and expression of Oct4 in early neural stem cells.

PubMed

Lee, Shih-Han; Jeyapalan, Jennie N; Appleby, Vanessa; Mohamed Noor, Dzul Azri; Sottile, Virginie; Scotting, Paul J

2010-09-01

Neural stem cells are a multipotent population of tissue-specific stem cells with a broad but limited differentiation potential. However, recent studies have shown that over-expression of the pluripotency gene, Oct4, alone is sufficient to initiate a process by which these can form 'induced pluripotent stem cells' (iPS cells) with the same broad potential as embryonic stem cells. This led us to examine the expression of Oct4 in endogenous neural stem cells, as data regarding its expression in neural stem cells in vivo are contradictory and incomplete. In this study we have therefore analysed the expression of Oct4 and other genes associated with pluripotency throughout development of the mouse CNS and in neural stem cells grown in vitro. We find that Oct4 is still expressed in the CNS by E8.5, but that this expression declines rapidly until it is undetectable by E15.5. This decline is coincident with the gradual methylation of the Oct4 promoter and proximal enhancer. Immunostaining suggests that the Oct4 protein is predominantly cytoplasmic in location. We also found that neural stem cells from all ages expressed the pluripotency associated genes, Sox2, c-Myc, Klf4 and Nanog. These data provide an explanation for the varying behaviour of cells from the early neuroepithelium at different stages of development. The expression of these genes also provides an indication of why Oct4 alone is sufficient to induce iPS formation in neural stem cells at later stages.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

PubMed

Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

2018-02-01

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

PubMed

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

2016-11-14

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

Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

PubMed

Petrican, Raluca; Grady, Cheryl L

2017-08-09

Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample ( N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior. SIGNIFICANCE STATEMENT The brain's functional

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

Induced neural stem cells achieve long-term survival and functional integration in the adult mouse brain.

PubMed

Hemmer, Kathrin; Zhang, Mingyue; van Wüllen, Thea; Sakalem, Marna; Tapia, Natalia; Baumuratov, Aidos; Kaltschmidt, Christian; Kaltschmidt, Barbara; Schöler, Hans R; Zhang, Weiqi; Schwamborn, Jens C

2014-09-09

Differentiated cells can be converted directly into multipotent neural stem cells (i.e., induced neural stem cells [iNSCs]). iNSCs offer an attractive alternative to induced pluripotent stem cell (iPSC) technology with regard to regenerative therapies. Here, we show an in vivo long-term analysis of transplanted iNSCs in the adult mouse brain. iNSCs showed sound in vivo long-term survival rates without graft overgrowths. The cells displayed a neural multilineage potential with a clear bias toward astrocytes and a permanent downregulation of progenitor and cell-cycle markers, indicating that iNSCs are not predisposed to tumor formation. Furthermore, the formation of synaptic connections as well as neuronal and glial electrophysiological properties demonstrated that differentiated iNSCs migrated, functionally integrated, and interacted with the existing neuronal circuitry. We conclude that iNSC long-term transplantation is a safe procedure; moreover, it might represent an interesting tool for future personalized regenerative applications. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

What’s bad in cancer is good in the embryo: Importance of EMT in neural crest development

PubMed Central

Kerosuo, Laura; Bronner-Fraser, Marianne

2012-01-01

Although the epithelial to mesenchymal transition (EMT) is famous for its role in cancer metastasis, it also is a normal developmental event in which epithelial cells are converted into migratory mesenchymal cells. A prime example of EMT during development occurs when neural crest (NC) cells emigrate from the neural tube thus providing an excellent model to study the principles of EMT in a nonmalignant environment. NC cells start life as neuroepithelial cells intermixed with precursors of the central nervous system. After EMT, they delaminate and begin migrating, often to distant sites in the embryo. While proliferating and maintaining multipotency and cell survival the transitioning neural crest cells lose apicobasal polarity and the basement membrane is broken down. This review discusses how these events are coordinated and regulated, by series of events involving signaling factors, gene regulatory interactions, as well as epigenetic and post-transcriptional modifications. Even though the series of events involved in NC EMT are well known, the sequence in which these steps take place remains a subject of debate, raising the intriguing possibility that, rather than being a single event, neural crest EMT may involve multiple parallel mechanisms. PMID:22430756

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

Distributed formation control of nonholonomic autonomous vehicle via RBF neural network

NASA Astrophysics Data System (ADS)

Yang, Shichun; Cao, Yaoguang; Peng, Zhaoxia; Wen, Guoguang; Guo, Konghui

2017-03-01

In this paper, RBF neural network consensus-based distributed control scheme is proposed for nonholonomic autonomous vehicles in a pre-defined formation along the specified reference trajectory. A variable transformation is first designed to convert the formation control problem into a state consensus problem. Then, the complete dynamics of the vehicles including inertia, Coriolis, friction model and unmodeled bounded disturbances are considered, which lead to the formation unstable when the distributed kinematic controllers are proposed based on the kinematics. RBF neural network torque controllers are derived to compensate for them. Some sufficient conditions are derived to accomplish the asymptotically stability of the systems based on algebraic graph theory, matrix theory, and Lyapunov theory. Finally, simulation examples illustrate the effectiveness of the proposed controllers.

Neural computing for numeric-to-symbolic conversion in control systems

NASA Technical Reports Server (NTRS)

Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

1989-01-01

A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

Neural control of the kidney: past, present, and future.

PubMed

DiBona, Gerald F

2003-03-01

This article provides a chronological perspective on the development of knowledge concerning the neural control of renal function and is divided into three parts: the past, the present, and the future.

Neural-activity mapping of memory-based dominance in the crow: neural networks integrating individual discrimination and social behaviour control.

PubMed

Nishizawa, K; Izawa, E-I; Watanabe, S

2011-12-01

Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

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.

Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

NASA Astrophysics Data System (ADS)

Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

2018-01-01

In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

Changes of neural markers expression during late neurogenic differentiation of human adipose-derived stem cells

PubMed Central

Razavi, Shahnaz; Khosravizadeh, Zahra; Bahramian, Hamid; Kazemi, Mohammad

2015-01-01

Background: Different studies have been done to obtain sufficient number of neural cells for treatment of neurodegenerative diseases, spinal cord, and traumatic brain injury because neural stem cells are limited in central nerves system. Recently, several studies have shown that adipose-derived stem cells (ADSCs) are the appropriate source of multipotent stem cells. Furthermore, these cells are found in large quantities. The aim of this study was an assessment of proliferation and potential of neurogenic differentiation of ADSCs with passing time. Materials and Methods: Neurosphere formation was used for neural induction in isolated human ADSCs (hADSCs). The rate of proliferation was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and potential of neural differentiation of induced hADSCs was evaluated by immunocytochemical and real-time reverse transcription polymerase chain reaction analysis after 10 and 14 days post-induction. Results: The rate of proliferation of induced hADSCs increased after 14 days while the expression of nestin, glial fibrillary acidic protein, and microtubule-associated protein 2 was decreased with passing time during neurogenic differentiation. Conclusion: These findings showed that the proliferation of induced cells increased with passing time, but in early neurogenic differentiation of hADSCs, neural expression was higher than late of differentiation. Thus, using of induced cells in early differentiation may be suggested for in vivo application. PMID:26605238

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.

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

PubMed Central

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. PMID:26042002

The optimization of force inputs for active structural acoustic control using a neural network

NASA Technical Reports Server (NTRS)

Cabell, R. H.; Lester, H. C.; Silcox, R. J.

1992-01-01

This paper investigates the use of a neural network to determine which force actuators, of a multi-actuator array, are best activated in order to achieve structural-acoustic control. The concept is demonstrated using a cylinder/cavity model on which the control forces, produced by piezoelectric actuators, are applied with the objective of reducing the interior noise. A two-layer neural network is employed and the back propagation solution is compared with the results calculated by a conventional, least-squares optimization analysis. The ability of the neural network to accurately and efficiently control actuator activation for interior noise reduction is demonstrated.

Automatic gain control of neural coupling during cooperative hand movements.

PubMed

Thomas, F A; Dietz, V; Schrafl-Altermatt, M

2018-04-13

Cooperative hand movements (e.g. opening a bottle) are controlled by a task-specific neural coupling, reflected in EMG reflex responses contralateral to the stimulation site. In this study the contralateral reflex responses in forearm extensor muscles to ipsilateral ulnar nerve stimulation was analyzed at various resistance and velocities of cooperative hand movements. The size of contralateral reflex responses was closely related to the level of forearm muscle activation required to accomplish the various cooperative hand movement tasks. This indicates an automatic gain control of neural coupling that allows a rapid matching of corrective forces exerted at both sides of an object with the goal 'two hands one action'.

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

PubMed

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

2017-12-01

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

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

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

PubMed

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

2012-10-01

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 and 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. Copyright © 2012 John Wiley & Sons, Ltd.

Neural control and transient analysis of the LCL-type resonant converter

NASA Astrophysics Data System (ADS)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

PubMed

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

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.

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.

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

PubMed

Lin, Chuan-Kai; Wang, Sheng-De

2004-11-01

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

MLL-ENL cooperates with SCF to transform primary avian multipotent cells.

PubMed

Schulte, Cathleen E; von Lindern, Marieke; Steinlein, Peter; Beug, Hartmut; Wiedemann, Leanne M

2002-08-15

The MLL gene is targeted by chromosomal translocations, which give rise to heterologous MLL fusion proteins and are associated with distinct types of acute lymphoid and myeloid leukaemia. To determine how MLL fusion proteins alter the proliferation and/or differentiation of primary haematopoietic progenitors, we introduced the MLL-AF9 and MLL-ENL fusion proteins into primary chicken bone marrow cells. Both fusion proteins caused the sustained outgrowth of immature haematopoietic cells, which was strictly dependent on stem cell factor (SCF). The renewing cells have a long in vitro lifespan exceeding the Hayflick limit of avian cells. Analysis of clonal cultures identified the renewing cells as immature, multipotent progenitors, expressing erythroid, myeloid, lymphoid and stem cell surface markers. Employing a two-step commitment/differentiation protocol involving the controlled withdrawal of SCF, the MLL-ENL-transformed progenitors could be induced to terminal erythroid or myeloid differentiation. Finally, in cooperation with the weakly leukaemogenic receptor tyrosine kinase v-Sea, the MLL-ENL fusion protein gave rise to multilineage leukaemia in chicks, suggesting that other activated, receptor tyrosine kinases can substitute for ligand-activated c-Kit in vivo.

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.

Neural mechanisms underlying auditory feedback control of speech

PubMed Central

Reilly, Kevin J.; Guenther, Frank H.

2013-01-01

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 135 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. PMID:18035557

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.

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

PubMed

Long, Lijun; Zhao, Jun

2017-07-01

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

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

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.

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

2014-07-22

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.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

PubMed

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

2015-03-01

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

Neural manufacturing: a novel concept for processing modeling, monitoring, and control

NASA Astrophysics Data System (ADS)

Fu, Chi Y.; Petrich, Loren; Law, Benjamin

1995-09-01

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.

Algebraic and adaptive learning in neural control systems

NASA Astrophysics Data System (ADS)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

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

Oxygen-controlled automated neural differentiation of mouse embryonic stem cells.

PubMed

Mondragon-Teran, Paul; Tostoes, Rui; Mason, Chris; Lye, Gary J; Veraitch, Farlan S

2013-03-01

Automation and oxygen tension control are two tools that provide significant improvements to the reproducibility and efficiency of stem cell production processes. the aim of this study was to establish a novel automation platform capable of controlling oxygen tension during both the cell-culture and liquid-handling steps of neural differentiation processes. We built a bespoke automation platform, which enclosed a liquid-handling platform in a sterile, oxygen-controlled environment. An airtight connection was used to transfer cell culture plates to and from an automated oxygen-controlled incubator. Our results demonstrate that our system yielded comparable cell numbers, viabilities, metabolism profiles and differentiation efficiencies when compared with traditional manual processes. Interestingly, eliminating exposure to ambient conditions during the liquid-handling stage resulted in significant improvements in the yield of MAP2-positive neural cells, indicating that this level of control can improve differentiation processes. This article describes, for the first time, an automation platform capable of maintaining oxygen tension control during both the cell-culture and liquid-handling stages of a 2D embryonic stem cell differentiation process.

Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

PubMed

Chen, Zhenfeng; Ge, Shuzhi Sam; Zhang, Yun; Li, Yanan

2014-11-01

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

Neural basis of bilingual language control.

PubMed

Calabria, Marco; Costa, Albert; Green, David W; Abutalebi, Jubin

2018-06-19

Acquiring and speaking a second language increases demand on the processes of language control for bilingual as compared to monolingual speakers. Language control for bilingual speakers involves the ability to keep the two languages separated to avoid interference and to select one language or the other in a given conversational context. This ability is what we refer with the term "bilingual language control" (BLC). It is now well established that the architecture of this complex system of language control encompasses brain networks involving cortical and subcortical structures, each responsible for different cognitive processes such as goal maintenance, conflict monitoring, interference suppression, and selective response inhibition. Furthermore, advances have been made in determining the overlap between the BLC and the nonlinguistic executive control networks, under the hypothesis that the BLC processes are just an instantiation of a more domain-general control system. Here, we review the current knowledge about the neural basis of these control systems. Results from brain imaging studies of healthy adults and on the performance of bilingual individuals with brain damage are discussed. © 2018 New York Academy of Sciences.

Decoupling control of vehicle chassis system based on neural network inverse system

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

Robust Neural Sliding Mode Control of Robot Manipulators

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.

Maximum entropy models as a tool for building precise neural controls.

PubMed

Savin, Cristina; Tkačik, Gašper

2017-10-01

Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible. Copyright © 2017 Elsevier Ltd. All rights reserved.

New multipotent tetracyclic tacrines with neuroprotective activity.

PubMed

Marco-Contelles, José; León, Rafael; de los Ríos, Cristóbal; García, Antonio G; López, Manuela G; Villarroya, Mercedes

2006-12-15

The synthesis and the biological evaluation (neuroprotection, voltage dependent calcium channel blockade, AChE/BuChE inhibitory activity and propidium binding) of new multipotent tetracyclic tacrine analogues (5-13) are described. Compounds 7, 8 and 11 showed a significant neuroprotective effect on neuroblastoma cells subjected to Ca(2+) overload or free radical induced toxicity. These compounds are modest AChE inhibitors [the best inhibitor (11) is 50-fold less potent than tacrine], but proved to be very selective, as for most of them no BuChE inhibition was observed. In addition, the propidium displacement experiments showed that these compounds bind AChE to the peripheral anionic site (PAS) of AChE and, consequently, are potential agents that can prevent the aggregation of beta-amyloid. Overall, compound 8 is a modest and selective AChE inhibitor, but an efficient neuroprotective agent against 70mM K(+) and 60microM H(2)O(2). Based on these results, some of these molecules can be considered as lead candidates for the further development of anti-Alzheimer drugs.

The relevance of central command for the neural cardiovascular control of exercise.

PubMed

Williamson, J W

2010-11-01

This paper briefly reviews the role of central command in the neural control of the circulation during exercise. While defined as a feedforward component of the cardiovascular control system, central command is also associated with perception of effort or effort sense. The specific factors influencing perception of effort and their effect on autonomic regulation of cardiovascular function during exercise can vary according to condition. Centrally mediated integration of multiple signals occurring during exercise certainly involves feedback mechanisms, but it is unclear whether or how these signals modify central command via their influence on perception of effort. As our understanding of central neural control systems continues to develop, it will be important to examine more closely how multiple sensory signals are prioritized and processed centrally to modulate cardiovascular responses during exercise. The purpose of this article is briefly to review the concepts underlying central command and its assessment via perception of effort, and to identify potential areas for future studies towards determining the role and relevance of central command for neural control of exercise.

The relevance of central command for the neural cardiovascular control of exercise

PubMed Central

Williamson, J W

2010-01-01

This paper briefly reviews the role of central command in the neural control of the circulation during exercise. While defined as a feedfoward component of the cardiovascular control system, central command is also associated with perception of effort or effort sense. The specific factors influencing perception of effort and their effect on autonomic regulation of cardiovascular function during exercise can vary according to condition. Centrally mediated integration of multiple signals occurring during exercise certainly involves feedback mechanisms, but it is unclear whether or how these signals modify central command via their influence on perception of effort. As our understanding of central neural control systems continues to develop, it will be important to examine more closely how multiple sensory signals are prioritized and processed centrally to modulate cardiovascular responses during exercise. The purpose of this article is briefly to review the concepts underlying central command and its assessment via perception of effort, and to identify potential areas for future studies towards determining the role and relevance of central command for neural control of exercise. PMID:20696787

Optical Neural Interfaces

PubMed Central

Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

2014-01-01

Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals. PMID:25014785

Towards a general neural controller for quadrupedal locomotion.

PubMed

Maufroy, Christophe; Kimura, Hiroshi; Takase, Kunikatsu

2008-05-01

Our study aims at the design and implementation of a general controller for quadruped locomotion, allowing the robot to use the whole range of quadrupedal gaits (i.e. from low speed walking to fast running). A general legged locomotion controller must integrate both posture control and rhythmic motion control and have the ability to shift continuously from one control method to the other according to locomotion speed. We are developing such a general quadrupedal locomotion controller by using a neural model involving a CPG (Central Pattern Generator) utilizing ground reaction force sensory feedback. We used a biologically faithful musculoskeletal model with a spine and hind legs, and computationally simulated stable stepping motion at various speeds using the neuro-mechanical system combining the neural controller and the musculoskeletal model. We compared the changes of the most important locomotion characteristics (stepping period, duty ratio and support length) according to speed in our simulations with the data on real cat walking. We found similar tendencies for all of them. In particular, the swing period was approximately constant while the stance period decreased with speed, resulting in a decreasing stepping period and duty ratio. Moreover, the support length increased with speed due to the posterior extreme position that shifted progressively caudally, while the anterior extreme position was approximately constant. This indicates that we succeeded in reproducing to some extent the motion of a cat from the kinematical point of view, even though we used a 2D bipedal model. We expect that such computational models will become essential tools for legged locomotion neuroscience in the future.

Neural control of finger movement via intracortical brain-machine interface

NASA Astrophysics Data System (ADS)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

2017-12-01

Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

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

PubMed

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

2014-04-30

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

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.

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. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

PubMed

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

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. Copyright © 2012 Elsevier Ltd. All rights reserved.

Novel neural control for a class of uncertain pure-feedback systems.

PubMed

Shen, Qikun; Shi, Peng; Zhang, Tianping; Lim, Cheng-Chew

2014-04-01

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.

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.

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

Neural mechanisms of interference control in working memory capacity.

PubMed

Bomyea, Jessica; Taylor, Charles T; Spadoni, Andrea D; Simmons, Alan N

2018-02-01

The extent to which one can use cognitive resources to keep information in working memory is known to rely on (1) active maintenance of target representations and (2) downregulation of interference from irrelevant representations. Neurobiologically, the global capacity of working memory is thought to depend on the prefrontal and parietal cortices; however, the neural mechanisms involved in controlling interference specifically in working memory capacity tasks remain understudied. In this study, 22 healthy participants completed a modified complex working memory capacity task (Reading Span) with trials of varying levels of interference control demands while undergoing functional MRI. Neural activity associated with interference control demands was examined separately during encoding and recall phases of the task. Results suggested a widespread network of regions in the prefrontal, parietal, and occipital cortices, and the cingulate and cerebellum associated with encoding, and parietal and occipital regions associated with recall. Results align with prior findings emphasizing the importance of frontoparietal circuits for working memory performance, including the role of the inferior frontal gyrus, cingulate, occipital cortex, and cerebellum in regulation of interference demands. © 2017 Wiley Periodicals, Inc.

Self-teaching neural network learns difficult reactor control problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.

[Isolation and Characterization of Multipotent Precursor Cells from Murine Adipose Tissue using a Clinically Approved Cell Separation System].

PubMed

Krug, C; Beer, A; Saller, M M; Aszodi, A; Holzbach, T; Giunta, R E; Volkmer, E

2016-04-01

Recent studies underscored the clinical potential of adipose-derived multipotent stem-/precursor cells (ASPCs). One of the main hurdles en route to clinical application was to isolate cells without having to perform expansion cultures outside the OR. A new generation of clinically approved, commercially available cell separation systems claims to provide ASPCs ready for application without further expansion cultures. However, it is unclear if the new systems yield sufficient cells of adequate quality for the use in autologous murine models. The aim of this study was to isolate and characterize adipose-derived precursor cells taken from the inguinal fat pat of wistar rats using InGeneron's clinically approved ARC™-cell separation system. We isolated cells from the inguinal fat pad of 3 male Wistar rats according to the manufacturer's protocol. In order to reduce the influence of the atmospheric oxygen on the multipotent precursor cells, one half of the cell suspension was cultivated under hypoxia (2% O2) simulating physiological conditions for ASPCs. As a control, the other half of the cells were cultivated under normoxia (21% O2). Cell surface markers CD90, CD29, CD45 and CD11b/c were analyzed by FACS, and osteogenic and adipogenic differentiation of the ASPCs was performed. Finally, cellular growth characteristics were assessed by evaluation of the cumulative population doublings and CFU assay, and metabolic activity was evaluated by WST-1 assay. Processing time was 90 (± 12) min. 1 g of adipose tissue yielded approximately 60 000 plastic adhering cells. Both groups showed a high expression of the mesenchymal stem cell markers CD90 and CD29 while they were negative for the leucocyte markers CD45 and CD11b/c. A strong osteogenic differentiation and a sufficient adipogenic differentiation potential was proven for all ASPCs. Under hypoxia, ASPCs showed increased proliferation characteristics and CFU efficiency as well as a significantly increased metabolic

Bioelectrochemical control of neural cell development on conducting polymers.

PubMed

Collazos-Castro, Jorge E; Polo, José L; Hernández-Labrado, Gabriel R; Padial-Cañete, Vanesa; García-Rama, Concepción

2010-12-01

Electrically conducting polymers hold promise for developing advanced neuroprostheses, bionic systems and neural repair devices. Among them, poly(3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) exhibits superior physicochemical properties but biocompatibility issues have limited its use. We describe combinations of electrochemical and molecule self-assembling methods to consistently control neural cell development on PEDOT:PSS while maintaining very low interfacial impedance. Electro-adsorbed polylysine enabled long-term neuronal survival and growth on the nanostructured polymer. Neurite extension was strongly inhibited by an additional layer of PSS or heparin, which in turn could be either removed electrically or further coated with spermine to activate cell growth. Binding basic fibroblast growth factor (bFGF) to the heparin layer inhibited neurons but promoted proliferation and migration of precursor cells. This methodology may orchestrate neural cell behavior on electroactive polymers, thus improving cell/electrode communication in prosthetic devices and providing a platform for tissue repair strategies. Copyright © 2010 Elsevier Ltd. All rights reserved.

On the neural control of social emotional behavior

PubMed Central

Roelofs, Karin; Minelli, Alessandra; Mars, Rogier B.; van Peer, Jacobien; Toni, Ivan

2009-01-01

It is known that the orbitofrontal cortex (OFC) is crucially involved in emotion regulation. However, the specific role of the OFC in controlling the behavior evoked by these emotions, such as approach–avoidance (AA) responses, remains largely unexplored. We measured behavioral and neural responses (using fMRI) during the performance of a social task, a reaction time (RT) task where subjects approached or avoided visually presented emotional faces by pulling or pushing a joystick, respectively. RTs were longer for affect-incongruent responses (approach angry faces and avoid happy faces) as compared to affect-congruent responses (approach–happy; avoid–angry). Moreover, affect-incongruent responses recruited increased activity in the left lateral OFC. These behavioral and neural effects emerged only when the subjects responded explicitly to the emotional value of the faces (AA-task) and largely disappeared when subjects responded to an affectively irrelevant feature of the faces during a control (gender evaluation: GE) task. Most crucially, the size of the OFC-effect correlated positively with the size of the behavioral costs of approaching angry faces. These findings qualify the role of the lateral OFC in the voluntary control of social–motivational behavior, emphasizing the relevance of this region for selecting rule-driven stimulus–response associations, while overriding automatic (affect-congruent) stimulus–response mappings. PMID:19047074

Multipotent cells from the human third molar: feasibility of cell-based therapy for liver disease.

PubMed

Ikeda, Etsuko; Yagi, Kiyohito; Kojima, Midori; Yagyuu, Takahiro; Ohshima, Akira; Sobajima, Satoshi; Tadokoro, Mika; Katsube, Yoshihiro; Isoda, Katsuhiro; Kondoh, Masuo; Kawase, Masaya; Go, Masahiro J; Adachi, Hisashi; Yokota, Yukiharu; Kirita, Tadaaki; Ohgushi, Hajime

2008-05-01

Adult stem cells have been reported to exist in various tissues. The isolation of high-quality human stem cells that can be used for regeneration of fatal deseases from accessible resources is an important advance in stem cell research. In the present study, we identified a novel stem cell, which we named tooth germ progenitor cells (TGPCs), from discarded third molar, commonly called as wisdom teeth. We demonstrated the characterization and distinctiveness of the TGPCs, and found that TGPCs showed high proliferation activity and capability to differentiate in vitro into cells of three germ layers including osteoblasts, neural cells, and hepatocytes. TGPCs were examined by the transplantation into a carbon tetrachloride (CCl4)-treated liver injured rat to determine whether this novel cell source might be useful for cell-based therapy to treat liver diseases. The successful engraftment of the TGPCs was demonstrated by PKH26 fluorescence in the recipient's rat as to liver at 4 weeks after transplantation. The TGPCs prevented the progression of liver fibrosis in the liver of CCl4-treated rats and contributed to the restoration of liver function, as assessed by the measurement of hepatic serum markers aspartate aminotransferase and alanine aminotransferase. Furthermore, the liver functions, observed by the levels of serum bilirubin and albumin, appeared to be improved following transplantation of TGPCs. These findings suggest that multipotent TGPCs are one of the candidates for cell-based therapy to treat liver diseases and offer unprecedented opportunities for developing therapies in treating tissue repair and regeneration.

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

Ionizing Radiation Perturbs Cell Cycle Progression of Neural Precursors in the Subventricular Zone Without Affecting Their Long-Term Self-Renewal

PubMed Central

Chen, Hongxin; Goodus, Matthew T; de Toledo, Sonia M; Azzam, Edouard I; Levison, Steven W

2015-01-01

Damage to normal human brain cells from exposure to ionizing radiation may occur during the course of radiotherapy or from accidental exposure. Delayed effects may complicate the immediate effects resulting in neurodegeneration and cognitive decline. We examined cellular and molecular changes associated with exposure of neural stem/progenitor cells (NSPs) to 137Cs γ-ray doses in the range of 0 to 8 Gy. Subventricular zone NSPs isolated from newborn mouse pups were analyzed for proliferation, self-renewal, and differentiation, shortly after irradiation. Strikingly, there was no apparent increase in the fraction of dying cells after irradiation, and the number of single cells that formed neurospheres showed no significant change from control. Upon differentiation, irradiated neural precursors did not differ in their ability to generate neurons, astrocytes, and oligodendrocytes. By contrast, progression of NSPs through the cell cycle decreased dramatically after exposure to 8 Gy (p < .001). Mice at postnatal day 10 were exposed to 8 Gy of γ rays delivered to the whole body and NSPs of the subventricular zone were analyzed using a four-color flow cytometry panel combined with ethynyl deoxyuridine incorporation. Similar flow cytometric analyses were performed on NSPs cultured as neurospheres. These studies revealed that neither the percentage of neural stem cells nor their proliferation was affected. By contrast, γ-irradiation decreased the proliferation of two classes of multipotent cells and increased the proliferation of a specific glial-restricted precursor. Altogether, these results support the conclusion that primitive neural precursors are radioresistant, but their proliferation is slowed down as a consequence of γ-ray exposure. PMID:26056396

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

PubMed

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

2014-01-01

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

A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

PubMed Central

Enderle, John D.

2014-01-01

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude. PMID:24944832

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

PubMed

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

2015-07-15

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in themore » closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.« less

Adult neural stem cells: The promise of the future

PubMed Central

Taupin, Philippe

2007-01-01

Stem cells are self-renewing undifferentiated cells that give rise to multiple types of specialized cells of the body. In the adult, stem cells are multipotents and contribute to homeostasis of the tissues and regeneration after injury. Until recently, it was believed that the adult brain was devoid of stem cells, hence unable to make new neurons and regenerate. With the recent evidences that neurogenesis occurs in the adult brain and neural stem cells (NSCs) reside in the adult central nervous system (CNS), the adult brain has the potential to regenerate and may be amenable to repair. The function(s) of NSCs in the adult CNS remains the source of intense research and debates. The promise of the future of adult NSCs is to redefine the functioning and physiopathology of the CNS, as well as to treat a broad range of CNS diseases and injuries. PMID:19300610

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.

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.

Deconstruction and Control of Neural Circuits in Posttraumatic Epilepsy

DTIC Science & Technology

2017-10-01

AWARD NUMBER: W81XWH-16-1-0576 TITLE: Deconstruction and Control of Neural Circuits in Posttraumatic Epilepsy PRINCIPAL INVESTIGATOR: Dr...official Department of the Army position, policy or decision unless so designated by other documentation. REPORT DOCUMENTATION PAGE Form Approved OMB...to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production.

PubMed

Waewsak, Chaiwat; Nopharatana, Annop; Chaiprasert, Pawinee

2010-01-01

Based on the developed neural-fuzzy control system for anaerobic hybrid reactor (AHR) in wastewater treatment and biogas production, the neural network with backpropagation algorithm for prediction of the variables pH, alkalinity (Alk) and total volatile acids (TVA) at present day time t was used as input data for the fuzzy logic to calculate the influent feed flow rate that was applied to control and monitor the process response at different operations in the initial, overload influent feeding and the recovery phases. In all three phases, this neural-fuzzy control system showed great potential to control AHR in high stability and performance and quick response. Although in the overloading operation phase II with two fold calculating influent flow rate together with a two fold organic loading rate (OLR), this control system had rapid response and was sensitive to the intended overload. When the influent feeding rate was followed by the calculation of control system in the initial operation phase I and the recovery operation phase III, it was found that the neural-fuzzy control system application was capable of controlling the AHR in a good manner with the pH close to 7, TVA/Alk < 0.4 and COD removal > 80% with biogas and methane yields at 0.45 and 0.30 m3/kg COD removed.

Prenatal Exposure to the Environmental Obesogen Tributyltin Predisposes Multipotent Stem Cells to Become Adipocytes

PubMed Central

Kirchner, Séverine; Kieu, Tiffany; Chow, Connie; Casey, Stephanie; Blumberg, Bruce

2010-01-01

The environmental obesogen hypothesis proposes that pre- and postnatal exposure to environmental chemicals contributes to adipogenesis and the development of obesity. Tributyltin (TBT) is an agonist of both retinoid X receptor (RXR) and peroxisome proliferator-activated receptor γ (PPARγ). Activation of these receptors can elevate adipose mass in adult mice exposed to the chemical in utero. Here we show that TBT sensitizes human and mouse multipotent stromal stem cells derived from white adipose tissue [adipose-derived stromal stem cells (ADSCs)] to undergo adipogenesis. In vitro exposure to TBT, or the PPARγ activator rosiglitazone increases adipogenesis, cellular lipid content, and expression of adipogenic genes. The adipogenic effects of TBT and rosiglitazone were blocked by the addition of PPARγ antagonists, suggesting that activation of PPARγ mediates the effect of both compounds on adipogenesis. ADSCs from mice exposed to TBT in utero showed increased adipogenic capacity and reduced osteogenic capacity with enhanced lipid accumulation in response to adipogenic induction. ADSCs retrieved from animals exposed to TBT in utero showed increased expression of PPARγ target genes such as the early adipogenic differentiation gene marker fatty acid-binding protein 4 and hypomethylation of the promoter/enhancer region of the fatty acid-binding protein 4 locus. Hence, TBT alters the stem cell compartment by sensitizing multipotent stromal stem cells to differentiate into adipocytes, an effect that could likely increase adipose mass over time. PMID:20160124

Prenatal exposure to the environmental obesogen tributyltin predisposes multipotent stem cells to become adipocytes.

PubMed

Kirchner, Séverine; Kieu, Tiffany; Chow, Connie; Casey, Stephanie; Blumberg, Bruce

2010-03-01

The environmental obesogen hypothesis proposes that pre- and postnatal exposure to environmental chemicals contributes to adipogenesis and the development of obesity. Tributyltin (TBT) is an agonist of both retinoid X receptor (RXR) and peroxisome proliferator-activated receptor gamma (PPARgamma). Activation of these receptors can elevate adipose mass in adult mice exposed to the chemical in utero. Here we show that TBT sensitizes human and mouse multipotent stromal stem cells derived from white adipose tissue [adipose-derived stromal stem cells (ADSCs)] to undergo adipogenesis. In vitro exposure to TBT, or the PPARgamma activator rosiglitazone increases adipogenesis, cellular lipid content, and expression of adipogenic genes. The adipogenic effects of TBT and rosiglitazone were blocked by the addition of PPARgamma antagonists, suggesting that activation of PPARgamma mediates the effect of both compounds on adipogenesis. ADSCs from mice exposed to TBT in utero showed increased adipogenic capacity and reduced osteogenic capacity with enhanced lipid accumulation in response to adipogenic induction. ADSCs retrieved from animals exposed to TBT in utero showed increased expression of PPARgamma target genes such as the early adipogenic differentiation gene marker fatty acid-binding protein 4 and hypomethylation of the promoter/enhancer region of the fatty acid-binding protein 4 locus. Hence, TBT alters the stem cell compartment by sensitizing multipotent stromal stem cells to differentiate into adipocytes, an effect that could likely increase adipose mass over time.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

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.

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.

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.

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller.

PubMed

von Twickel, Arndt; Büschges, Ansgar; Pasemann, Frank

2011-02-01

This article presents modular recurrent neural network controllers for single legs of a biomimetic six-legged robot equipped with standard DC motors. Following arguments of Ekeberg et al. (Arthropod Struct Dev 33:287-300, 2004), completely decentralized and sensori-driven neuro-controllers were derived from neuro-biological data of stick-insects. Parameters of the controllers were either hand-tuned or optimized by an evolutionary algorithm. Employing identical controller structures, qualitatively similar behaviors were achieved for robot and for stick insect simulations. For a wide range of perturbing conditions, as for instance changing ground height or up- and downhill walking, swing as well as stance control were shown to be robust. Behavioral adaptations, like varying locomotion speeds, could be achieved by changes in neural parameters as well as by a mechanical coupling to the environment. To a large extent the simulated walking behavior matched biological data. For example, this was the case for body support force profiles and swing trajectories under varying ground heights. The results suggest that the single-leg controllers are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.

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.

Vibration control of building structures using self-organizing and self-learning neural networks

NASA Astrophysics Data System (ADS)

Madan, Alok

2005-11-01

Past research in artificial intelligence establishes that artificial neural networks (ANN) are effective and efficient computational processors for performing a variety of tasks including pattern recognition, classification, associative recall, combinatorial problem solving, adaptive control, multi-sensor data fusion, noise filtering and data compression, modelling and forecasting. The paper presents a potentially feasible approach for training ANN in active control of earthquake-induced vibrations in building structures without the aid of teacher signals (i.e. target control forces). A counter-propagation neural network is trained to output the control forces that are required to reduce the structural vibrations in the absence of any feedback on the correctness of the output control forces (i.e. without any information on the errors in output activations of the network). The present study shows that, in principle, the counter-propagation network (CPN) can learn from the control environment to compute the required control forces without the supervision of a teacher (unsupervised learning). Simulated case studies are presented to demonstrate the feasibility of implementing the unsupervised learning approach in ANN for effective vibration control of structures under the influence of earthquake ground motions. The proposed learning methodology obviates the need for developing a mathematical model of structural dynamics or training a separate neural network to emulate the structural response for implementation in practice.

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.

Homeostatic control of neural activity: from phenomenology to molecular design.

PubMed

Davis, Graeme W

2006-01-01

Homeostasis is a specialized form of regulation that precisely maintains the function of a system at a set point level of activity. Recently, homeostatic signaling has been suggested to control neural activity through the modulation of synaptic efficacy and membrane excitability ( Davis & Goodman 1998a, Turrigiano & Nelson 2000, Marder & Prinz 2002, Perez-Otano & Ehlers 2005 ). In this way, homeostatic signaling is thought to constrain neural plasticity and contribute to the stability of neural function over time. Using a restrictive definition of homeostasis, this review first evaluates the phenomenological and molecular evidence for homeostatic signaling in the nervous system. Then, basic principles underlying the design and molecular implementation of homeostatic signaling are reviewed on the basis of work in other, simplified biological systems such as bacterial chemotaxis and the heat shock response. Data from these systems are then discussed in the context of homeostatic signaling in the nervous system.

Artificial neural networks in Space Station optimal attitude control

NASA Astrophysics Data System (ADS)

Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

1992-08-01

Innovative techniques of using 'Artificial Neural Networks' (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using Control Moment Gyros (CMGs) are investigated. The first technique uses a feedforward ANN with multilayer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second techique uses a single layer feedforward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

Brain vascular pericytes following ischemia have multipotential stem cell activity to differentiate into neural and vascular lineage cells.

PubMed

Nakagomi, Takayuki; Kubo, Shuji; Nakano-Doi, Akiko; Sakuma, Rika; Lu, Shan; Narita, Aya; Kawahara, Maiko; Taguchi, Akihiko; Matsuyama, Tomohiro

2015-06-01

Brain vascular pericytes (PCs) are a key component of the blood-brain barrier (BBB)/neurovascular unit, along with neural and endothelial cells. Besides their crucial role in maintaining the BBB, increasing evidence shows that PCs have multipotential stem cell activity. However, their multipotency has not been considered in the pathological brain, such as after an ischemic stroke. Here, we examined whether brain vascular PCs following ischemia (iPCs) have multipotential stem cell activity and differentiate into neural and vascular lineage cells to reconstruct the BBB/neurovascular unit. Using PCs extracted from ischemic regions (iPCs) from mouse brains and human brain PCs cultured under oxygen/glucose deprivation, we show that PCs developed stemness presumably through reprogramming. The iPCs revealed a complex phenotype of angioblasts, in addition to their original mesenchymal properties, and multidifferentiated into cells from both a neural and vascular lineage. These data indicate that under ischemic/hypoxic conditions, PCs can acquire multipotential stem cell activity and can differentiate into major components of the BBB/neurovascular unit. Thus, these findings support the novel concept that iPCs can contribute to both neurogenesis and vasculogenesis at the site of brain injuries. © 2015 AlphaMed Press.

Applications of Neural Networks to Adaptive Control

DTIC Science & Technology

1989-12-01

DTIC ;-E py 00 NAVAL POSTGRADUATE SCHOOL Monterey, California I.$ RDTIC IELECTE fl THESIS BEG7V°U APPLICATIONS OF NEURAL NETWORKS TO ADAPTIVE CONTROL...MONITORING ORGANIZATION Naval Postgraduate School (10 applicable) Naval Postgraduate School 6< ADDRESS (C~tV Start, and ZIPCode) 7b ADDRESS(City. Stott...eshausted SICURIIY CLAS$SItATO’. -r’, T..i5 PAGE~ All oth~er edit,ons art obsolete U11C1ASS 11- I’ Approved for Public release; distribution unlimited

How Tissue Mechanical Properties Affect Enteric Neural Crest Cell Migration

NASA Astrophysics Data System (ADS)

Chevalier, N. R.; Gazguez, E.; Bidault, L.; Guilbert, T.; Vias, C.; Vian, E.; Watanabe, Y.; Muller, L.; Germain, S.; Bondurand, N.; Dufour, S.; Fleury, V.

2016-02-01

Neural crest cells (NCCs) are a population of multipotent cells that migrate extensively during vertebrate development. Alterations to neural crest ontogenesis cause several diseases, including cancers and congenital defects, such as Hirschprung disease, which results from incomplete colonization of the colon by enteric NCCs (ENCCs). We investigated the influence of the stiffness and structure of the environment on ENCC migration in vitro and during colonization of the gastrointestinal tract in chicken and mouse embryos. We showed using tensile stretching and atomic force microscopy (AFM) that the mesenchyme of the gut was initially soft but gradually stiffened during the period of ENCC colonization. Second-harmonic generation (SHG) microscopy revealed that this stiffening was associated with a gradual organization and enrichment of collagen fibers in the developing gut. Ex-vivo 2D cell migration assays showed that ENCCs migrated on substrates with very low levels of stiffness. In 3D collagen gels, the speed of the ENCC migratory front decreased with increasing gel stiffness, whereas no correlation was found between porosity and ENCC migration behavior. Metalloprotease inhibition experiments showed that ENCCs actively degraded collagen in order to progress. These results shed light on the role of the mechanical properties of tissues in ENCC migration during development.

Cellular Analysis of Adult Neural Stem Cells for Investigating Prion Biology.

PubMed

Haigh, Cathryn L

2017-01-01

Traditional primary and secondary cell cultures have been used for the investigation of prion biology and disease for many years. While both types of cultures produce highly valid and immensely valuable results, they also have their limitations; traditional cell lines are often derived from cancers, therefore subject to numerous DNA changes, and primary cultures are labor-intensive and expensive to produce requiring sacrifice of many animals. Neural stem cell (NSC) cultures are a relatively new technology to be used for the study of prion biology and disease. While NSCs are subject to their own limitations-they are generally cultured ex vivo in environments that artificially force their growth-they also have their own unique advantages. NSCs retain the ability for self-renewal and can therefore be propagated in culture similarly to secondary cultures without genetic manipulation. In addition, NSCs are multipotent; they can be induced to differentiate into mature cells of central nervous system (CNS) linage. The combination of self-renewal and multipotency allows NSCs to be used as a primary cell line over multiple generations saving time, costs, and animal harvests, thus providing a valuable addition to the existing cell culture repertoire used for investigation of prion biology and disease. Furthermore, NSC cultures can be generated from mice of any genotype, either by embryonic harvest or harvest from adult brain, allowing gene expression to be studied without further genetic manipulation. This chapter describes a standard method of culturing adult NSCs and assays for monitoring NSC growth, migration, and differentiation and revisits basic reactive oxygen species detection in the context of NSC cultures.

Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

PubMed

Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

2017-09-01

This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Expansion and hepatic differentiation of rat multipotent adult progenitor cells in microcarrier suspension culture.

PubMed

Park, Y; Subramanian, K; Verfaillie, C M; Hu, W S

2010-10-01

Many potential applications of stem cells require large quantities of cells, especially those involving large organs such as the liver. For such applications, a scalable reactor system is desirable to ensure a reliable supply of sufficient quantities of differentiation competent or differentiated cells. We employed a microcarrier culture system for the expansion of undifferentiated rat multipotent adult progenitor cells (rMAPC) as well as for directed differentiation of these cells to hepatocyte-like cells. During the 4-day expansion culture, cell concentration increased by 85-fold while expression level of pluripotency markers were maintained, as well as the MAPC differentiation potential. Directed differentiation into hepatocyte-like cells on the microcarriers themselves gave comparable results as observed with cells cultured in static cultures. The cells expressed several mature hepatocyte-lineage genes and asialoglycoprotein receptor-1 (ASGPR-1) surface protein, and secreted albumin and urea. Microcarrier culture thus offers the potential of large-scale expansion and differentiation of stem cells in a more controlled bioreactor environment. Copyright © 2010 Elsevier B.V. All rights reserved.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

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

PubMed

Owald, David; Lin, Suewei; Waddell, Scott

2015-09-19

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.

Neural network based control of Doubly Fed Induction Generator in wind power generation

NASA Astrophysics Data System (ADS)

Barbade, Swati A.; Kasliwal, Prabha

2012-07-01

To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

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.

A continually online-trained neural network controller for brushless DC motor drives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rubaai, A.; Kotaru, R.; Kankam, M.D.

2000-04-01

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless dc motor drives. The dynamics of the motor/load are modeled online, and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This schemes incorporates three multilayered feedforward neural networks that are online trained, using the Levenburg-Marquadtmore » training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods. The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.« less

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.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

PubMed

Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

2014-04-01

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

Neural activation patterns during response inhibition distinguish adolescents with ADHD, their unaffected siblings, and healthy controls

PubMed Central

van Rooij, Daan; Hoekstra, Pieter J.; Mennes, Maarten; von Rhein, Daniel; Thissen, Andrieke J.A.M.; Heslenfeld, Dirk; Zwiers, Marcel P.; Faraone, Stephen V.; Oosterlaan, Jaap; Franke, Barbara; Rommelse, Nanda; Buitelaar, Jan K.; Hartman, Catharina A.

2015-01-01

Objective Impaired response inhibition is a key executive function deficit of attention-deficit/hyperactivity disorder (ADHD). Still, behavioral response inhibition measures do not consistently differentiate individuals with ADHD from unaffected individuals. We therefore investigated the neural correlates of response inhibition as well as the familial nature of these neural correlates. Methods fMRI measurements of neural activation during the stop-signal task along with behavioral measures of response inhibition were obtained in adolescents and young adults with ADHD (N=185), their unaffected siblings (N=111), and healthy controls (N=124). Results Stop-signal reaction times were longer in participants with ADHD, but not in their unaffected siblings, while reaction time variability and error rates were higher in both groups than in controls. Neural hypoactivation was observed in frontal-striatal and frontal-parietal networks of participants with ADHD and unaffected siblings compared to controls, whereby activation in inferior frontal and temporal/parietal nodes in unaffected siblings was intermediate between that of participants with ADHD and controls. Furthermore, neural activation in inferior frontal nodes correlated with stop-signal reaction times, and activation in both inferior frontal and temporal/parietal nodes correlated with ADHD severity. Conclusions Neural activation alterations in ADHD are more robust than behavioral response inhibition deficits and explain variance in response inhibition and ADHD severity. Although only affected participants with ADHD have deficient response inhibition, hypoactivation in inferior frontal and temporal-parietal nodes in unaffected siblings support the familial nature of the underlying neural process. Hypoactivation in these nodes may be useful as endophenotypes that extend beyond the affected individuals in the family. PMID:25615565

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

The nuclear hormone receptor family member NR5A2 controls aspects of multipotent progenitor cell formation and acinar differentiation during pancreatic organogenesis.

PubMed

Hale, Michael A; Swift, Galvin H; Hoang, Chinh Q; Deering, Tye G; Masui, Toshi; Lee, Youn-Kyoung; Xue, Jumin; MacDonald, Raymond J

2014-08-01

The orphan nuclear receptor NR5A2 is necessary for the stem-like properties of the epiblast of the pre-gastrulation embryo and for cellular and physiological homeostasis of endoderm-derived organs postnatally. Using conditional gene inactivation, we show that Nr5a2 also plays crucial regulatory roles during organogenesis. During the formation of the pancreas, Nr5a2 is necessary for the expansion of the nascent pancreatic epithelium, for the subsequent formation of the multipotent progenitor cell (MPC) population that gives rise to pre-acinar cells and bipotent cells with ductal and islet endocrine potential, and for the formation and differentiation of acinar cells. At birth, the NR5A2-deficient pancreas has defects in all three epithelial tissues: a partial loss of endocrine cells, a disrupted ductal tree and a >90% deficit of acini. The acinar defects are due to a combination of fewer MPCs, deficient allocation of those MPCs to pre-acinar fate, disruption of acinar morphogenesis and incomplete acinar cell differentiation. NR5A2 controls these developmental processes directly as well as through regulatory interactions with other pancreatic transcriptional regulators, including PTF1A, MYC, GATA4, FOXA2, RBPJL and MIST1 (BHLHA15). In particular, Nr5a2 and Ptf1a establish mutually reinforcing regulatory interactions and collaborate to control developmentally regulated pancreatic genes by binding to shared transcriptional regulatory regions. At the final stage of acinar cell development, the absence of NR5A2 affects the expression of Ptf1a and its acinar specific partner Rbpjl, so that the few acinar cells that form do not complete differentiation. Nr5a2 controls several temporally distinct stages of pancreatic development that involve regulatory mechanisms relevant to pancreatic oncogenesis and the maintenance of the exocrine phenotype. © 2014. Published by The Company of Biologists Ltd.

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…

Multipotent mesenchymal stromal cell sheet therapy for bisphosphonate-related osteonecrosis of the jaw in a rat model.

PubMed

Kaibuchi, Nobuyuki; Iwata, Takanori; Yamato, Masayuki; Okano, Teruo; Ando, Tomohiro

2016-09-15

Bisphosphonates (BPs) inhibit bone resorption and are frequently used to treat osteoporosis, bone metastasis, and other conditions that result in bone fragility. However, numerous studies have reported that BPs are closely related to the development of osteonecrosis of the jaw (BRONJ), which is an intractable disease. Recent studies have demonstrated that intravenous infusion of multipotent mesenchymal stromal cells (MSCs) is effective for the treatment of BRONJ-like disease models. However, the stability of injected MSCs is relatively low. In this study, the protein level of vascular endothelial growth factor in BP-treated MSCs was significantly lower than untreated-MSCs. The mRNA expression levels of receptor activator of nuclear factor κ-B ligand and osteoprotegerin were significantly decreased in BP-treated MSCs. We developed a tissue-engineered cell sheet of allogeneic enhanced green fluorescent protein (EGFP)-labeled MSCs and investigated the effect of MSC sheet transplantation in a BRONJ-like rat model. The MSC sheet group showed wound healing in most cases compared with the control group and MSC intravenous injection group (occurrence of bone exposure: 12.5% compared with 80% and 100%, respectively). Immunofluorescence staining revealed that EGFP-positive cells were localized around newly formed blood vessels in the transplanted sub-mucosa at 2weeks after transplantation. Blood vessels were significantly observed in the MSC sheet group compared to in the control group and MSC intravenous injection group (106±9.6 compared with 40±5.3 and 62±10.2 vessels/mm(2), respectively). These results suggest that allogeneic MSC sheet transplantation is a promising alternative approach for treating BRONJ. Bisphosphonates are frequently used to treat osteoporosis, bone metastasis of various cancers, and other diseases. However, bisphosphonate related-osteonecrosis of the jaw (BRONJ) is an intractable disease because it often recurs after surgery or is exacerbated

Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller.

PubMed

Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen

2018-06-01

The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Artificial neural networks in Space Station optimal attitude control

NASA Astrophysics Data System (ADS)

Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

1995-01-01

Innovative techniques of using "artificial neural networks" (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second technique uses a single layer feed-forward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

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…

Multipotent human stromal cells improve cardiac function after myocardial infarction in mice without long-term engraftment.

PubMed Central

Iso, Yoshitaka; Spees, Jeffrey L.; Serrano, Claudia; Bakondi, Benjamin; Pochampally, Radhika; Song, Yao-Hua; Sobel, Burton E.; Delafontaine, Patrick; Prockop, Darwin J.

2007-01-01

The aim of this study was to determine whether intravenously-administered multipotent stromal cells from human bone marrow (hMSCs) can improve cardiac function after myocardial infarction (MI) without long-term engraftment and therefore whether transitory paracrine effects or secreted factors are responsible for the benefit conferred. hMSCs were injected systemically into immunodeficient mice with acute MI. Cardiac function and fibrosis after MI in the hMSC-treated group was significantly improved compared with that in controls. However, despite the cardiac improvement, there was no evident hMSC engraftment in the heart 3 weeks after MI. Microarray assays and ELISAs demonstrated that multiple protective factors were expressed and secreted from the hMSCs in culture. Factors secreted by hMSCs prevented cell death of cultured cardiomyocytes and endothelial cells under conditions that mimicked tissue ischemia. The favorable effects of hMSCs appear to reflect the impact of secreted factors rather than engraftment, differentiation, or cell fusion. PMID:17257581

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

PubMed

Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

2017-03-01

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Sox5 Functions as a Fate Switch in Medaka Pigment Cell Development

PubMed Central

Nagao, Yusuke; Suzuki, Takao; Shimizu, Atsushi; Kimura, Tetsuaki; Seki, Ryoko; Adachi, Tomoko; Inoue, Chikako; Omae, Yoshihiro; Kamei, Yasuhiro; Hara, Ikuyo; Taniguchi, Yoshihito; Naruse, Kiyoshi; Wakamatsu, Yuko; Kelsh, Robert N.; Hibi, Masahiko; Hashimoto, Hisashi

2014-01-01

Mechanisms generating diverse cell types from multipotent progenitors are crucial for normal development. Neural crest cells (NCCs) are multipotent stem cells that give rise to numerous cell-types, including pigment cells. Medaka has four types of NCC-derived pigment cells (xanthophores, leucophores, melanophores and iridophores), making medaka pigment cell development an excellent model for studying the mechanisms controlling specification of distinct cell types from a multipotent progenitor. Medaka many leucophores-3 (ml-3) mutant embryos exhibit a unique phenotype characterized by excessive formation of leucophores and absence of xanthophores. We show that ml-3 encodes sox5, which is expressed in premigratory NCCs and differentiating xanthophores. Cell transplantation studies reveal a cell-autonomous role of sox5 in the xanthophore lineage. pax7a is expressed in NCCs and required for both xanthophore and leucophore lineages; we demonstrate that Sox5 functions downstream of Pax7a. We propose a model in which multipotent NCCs first give rise to pax7a-positive partially fate-restricted intermediate progenitors for xanthophores and leucophores; some of these progenitors then express sox5, and as a result of Sox5 action develop into xanthophores. Our results provide the first demonstration that Sox5 can function as a molecular switch driving specification of a specific cell-fate (xanthophore) from a partially-restricted, but still multipotent, progenitor (the shared xanthophore-leucophore progenitor). PMID:24699463

mNotch1 signaling and erythropoietin cooperate in erythroid differentiation of multipotent progenitor cells and upregulate beta-globin.

PubMed

Henning, Konstanze; Schroeder, Timm; Schwanbeck, Ralf; Rieber, Nikolaus; Bresnick, Emery H; Just, Ursula

2007-09-01

In many developing tissues, signaling mediated by activation of the transmembrane receptor Notch influences cell-fate decisions, differentiation, proliferation, and cell survival. Notch receptors are expressed on hematopoietic cells and cognate ligands on bone marrow stromal cells. Here, we investigate the role of mNotch1 signaling in the control of erythroid differentiation of multipotent progenitor cells. Multipotent FDCP-mix cell lines engineered to permit the conditional induction of the constitutively active intracellular domain of mNotch1 (mN1(IC)) by the 4-hydroxytamoxifen (OHT)-inducible system were used to analyze the effects of activated mNotch1 on erythroid differentiation and on expression of Gata1, Fog1, Eklf, NF-E2, and beta-globin. Expression was analyzed by Northern blotting and real-time polymerase chain reaction. Enhancer activity of reporter constructs was determined with the dual luciferase system in transient transfection assays. Induction of mN1(IC) by OHT resulted in increased and accelerated differentiation of FDCP-mix cells along the erythroid lineage. Erythroid maturation was induced by activated Notch1 also under conditions that normally promote self-renewal, but required the presence of erythropoietin for differentiation to proceed. While induction of Notch signaling rapidly upregulated Hes1 and Hey1 expression, the expression of Gata1, Fog1, Eklf, and NF-E2 remained unchanged. Concomitantly with erythroid differentiation, activated mNotch1 upregulated beta-globin RNA. Notch signaling transactivated a reporter construct harboring a conserved RBP-J (CBF1) binding site in the hypersensitive site 2 (HS2) of human beta-globin. Transactivation by activated Notch was completely abolished when this RBP-J site was mutated to prevent RBP-J binding. Our results show that activation of mNotch1 induces erythroid differentiation in cooperation with erythropoietin and upregulates beta-globin expression.

mSEL-1L (Suppressor/Enhancer Lin12-like) Protein Levels Influence Murine Neural Stem Cell Self-renewal and Lineage Commitment*

PubMed Central

Cardano, Marina; Diaferia, Giuseppe R.; Cattaneo, Monica; Dessì, Sara S.; Long, Qiaoming; Conti, Luciano; DeBlasio, Pasquale; Cattaneo, Elena; Biunno, Ida

2011-01-01

Murine SEL-1L (mSEL-1L) is a key component of the endoplasmic reticulum-associated degradation pathway. It is essential during development as revealed by the multi-organ dysfunction and in uterus lethality occurring in homozygous mSEL-1L-deficient mice. Here we show that mSEL-1L is highly expressed in pluripotent embryonic stem cells and multipotent neural stem cells (NSCs) but silenced in all mature neural derivatives (i.e. astrocytes, oligodendrocytes, and neurons) by mmu-miR-183. NSCs derived from homozygous mSEL-1L-deficient embryos (mSEL-1L−/− NSCs) fail to proliferate in vitro, show a drastic reduction of the Notch effector HES-5, and reveal a significant down-modulation of the early neural progenitor markers PAX-6 and OLIG-2, when compared with the wild type (mSEL-1L+/+ NSCs) counterpart. Furthermore, these cells are almost completely deprived of the neural marker Nestin, display a significant decrease of SOX-2 expression, and rapidly undergo premature astrocytic commitment and apoptosis. The data suggest severe self-renewal defects occurring in these cells probably mediated by misregulation of the Notch signaling. The results reported here denote mSEL-1L as a primitive marker with a possible involvement in the regulation of neural progenitor stemness maintenance and lineage determination. PMID:21454627

mSEL-1L (Suppressor/enhancer Lin12-like) protein levels influence murine neural stem cell self-renewal and lineage commitment.

PubMed

Cardano, Marina; Diaferia, Giuseppe R; Cattaneo, Monica; Dessì, Sara S; Long, Qiaoming; Conti, Luciano; Deblasio, Pasquale; Cattaneo, Elena; Biunno, Ida

2011-05-27

Murine SEL-1L (mSEL-1L) is a key component of the endoplasmic reticulum-associated degradation pathway. It is essential during development as revealed by the multi-organ dysfunction and in uterus lethality occurring in homozygous mSEL-1L-deficient mice. Here we show that mSEL-1L is highly expressed in pluripotent embryonic stem cells and multipotent neural stem cells (NSCs) but silenced in all mature neural derivatives (i.e. astrocytes, oligodendrocytes, and neurons) by mmu-miR-183. NSCs derived from homozygous mSEL-1L-deficient embryos (mSEL-1L(-/-) NSCs) fail to proliferate in vitro, show a drastic reduction of the Notch effector HES-5, and reveal a significant down-modulation of the early neural progenitor markers PAX-6 and OLIG-2, when compared with the wild type (mSEL-1L(+/+) NSCs) counterpart. Furthermore, these cells are almost completely deprived of the neural marker Nestin, display a significant decrease of SOX-2 expression, and rapidly undergo premature astrocytic commitment and apoptosis. The data suggest severe self-renewal defects occurring in these cells probably mediated by misregulation of the Notch signaling. The results reported here denote mSEL-1L as a primitive marker with a possible involvement in the regulation of neural progenitor stemness maintenance and lineage determination.

Transgenic analysis of a SoxB gene reveals neural progenitor cells in the cnidarian Nematostella vectensis.

PubMed

Richards, Gemma Sian; Rentzsch, Fabian

2014-12-01

Bilaterian neurogenesis is characterized by the generation of diverse neural cell types from dedicated neural stem/progenitor cells (NPCs). However, the evolutionary origin of NPCs is unclear, as neurogenesis in representatives of the bilaterian sister group, the Cnidaria, occurs via interstitial stem cells that also possess broader, non-neural, developmental potential. We address this question by analysing neurogenesis in an anthozoan cnidarian, Nematostella vectensis. Using a transgenic reporter line, we show that NvSoxB(2) - an orthologue of bilaterian SoxB genes that have conserved roles in neurogenesis - is expressed in a cell population that gives rise to sensory neurons, ganglion neurons and nematocytes: the three primary neural cell types of cnidarians. EdU labelling together with in situ hybridization, and within the NvSoxB(2)::mOrange transgenic line, demonstrates that cells express NvSoxB(2) before mitosis and identifies asymmetric behaviours of sibling cells within NvSoxB(2)(+) lineages. Morpholino-mediated gene knockdown of NvSoxB(2) blocks the formation of all three neural cell types, thereby identifying NvSoxB(2) as an essential positive regulator of nervous system development. Our results demonstrate that diverse neural cell types derive from an NvSoxB(2)-expressing population of mitotic cells in Nematostella and that SoxB genes are ancient components of a neurogenic program. To our knowledge this is the first description of a lineage-restricted, multipotent cell population outside the Bilateria and we propose that neurogenesis via dedicated, SoxB-expressing NPCs predates the split between cnidarians and bilaterians. © 2014. Published by The Company of Biologists Ltd.

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

Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

NASA Astrophysics Data System (ADS)

Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

2017-03-01

Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Neural-network-designed pulse sequences for robust control of singlet-triplet qubits

NASA Astrophysics Data System (ADS)

Yang, Xu-Chen; Yung, Man-Hong; Wang, Xin

2018-04-01

Composite pulses are essential for universal manipulation of singlet-triplet spin qubits. In the absence of noise, they are required to perform arbitrary single-qubit operations due to the special control constraint of a singlet-triplet qubit, while in a noisy environment, more complicated sequences have been developed to dynamically correct the error. Tailoring these sequences typically requires numerically solving a set of nonlinear equations. Here we demonstrate that these pulse sequences can be generated by a well-trained, double-layer neural network. For sequences designed for the noise-free case, the trained neural network is capable of producing almost exactly the same pulses known in the literature. For more complicated noise-correcting sequences, the neural network produces pulses with slightly different line shapes, but the robustness against noises remains comparable. These results indicate that the neural network can be a judicious and powerful alternative to existing techniques in developing pulse sequences for universal fault-tolerant quantum computation.

Remote Neural Pendants In A Welding-Control System

NASA Technical Reports Server (NTRS)

Venable, Richard A.; Bucher, Joseph H.

1995-01-01

Neural network integrated circuits enhance functionalities of both remote terminals (called "pendants") and communication links, without necessitating installation of additional wires in links. Makes possible to incorporate many features into pendant, including real-time display of critical welding parameters and other process information, capability for communication between technician at pendant and host computer or technician elsewhere in system, and switches and potentiometers through which technician at pendant exerts remote control over such critical aspects of welding process as current, voltage, rate of travel, flow of gas, starting, and stopping. Other potential manufacturing applications include control of spray coating and of curing of composite materials. Potential nonmanufacturing uses include remote control of heating, air conditioning, and lighting in electrically noisy and otherwise hostile environments.

Neural network control of focal position during time-lapse microscopy of cells.

PubMed

Wei, Ling; Roberts, Elijah

2018-05-09

Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.

No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control

PubMed Central

Saunders, Blair; He, Frank F. H.; Inzlicht, Michael

2015-01-01

It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude—operationalized by experimental condition or as a continuous predictor—and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes. PMID:26633830

No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control.

PubMed

Saunders, Blair; He, Frank F H; Inzlicht, Michael

2015-01-01

It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude--operationalized by experimental condition or as a continuous predictor--and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes.

SIRT1 directly regulates SOX2 to maintain self-renewal and multipotency in bone marrow-derived mesenchymal stem cells.

PubMed

Yoon, Dong Suk; Choi, Yoorim; Jang, Yeonsue; Lee, Moses; Choi, Woo Jin; Kim, Sung-Hwan; Lee, Jin Woo

2014-12-01

SOX2 is crucial for the maintenance of the self-renewal capacity and multipotency of mesenchymal stem cells (MSCs); however, the mechanism by which SOX2 is regulated remains unclear. Here, we report that RNA interference of sirtuin 1 (SIRT1) in human bone marrow (BM)-derived MSCs leads to a decrease of SOX2 protein, resulting in the deterioration of the self-renewal and differentiation capacities of BM-MSCs. Using immunoprecipitation, we demonstrated direct binding between SIRT1 and SOX2 in HeLa cells overexpressing SOX2. We further discovered that the RNA interference of SIRT1 induces the acetylation, nuclear export, and ubiquitination of SOX2, leading to proteasomal degradation in BM-MSCs. SOX2 suppression by trichostatin A (TSA), a known histone deacetylase inhibitor, was reverted by treatment with resveratrol (0.1 and 1 µM), a known activator of SIRT1 in BM-MSCs. Furthermore, 0.1 and 1 µM resveratrol reduced TSA-mediated acetylation and ubiquitination of SOX2 in BM-MSCs. SIRT1 activation by resveratrol enhanced the colony-forming ability and differentiation potential to osteogenic and adipogenic lineages in a dose-dependent manner. However, the enhancement of self-renewal and multipotency by resveratrol was significantly decreased to basal levels by RNA interference of SOX2. These results strongly suggest that the SIRT1-SOX2 axis plays an important role in maintaining the self-renewal capability and multipotency of BM-MSCs. In conclusion, our findings provide evidence for positive SOX2 regulation by post-translational modification in BM-MSCs through the inhibition of nuclear export and subsequent ubiquitination, and demonstrate that SIRT1-mediated deacetylation contributes to maintaining SOX2 protein in the nucleus. © 2014 AlphaMed Press.

Bone morphogenetic protein 9 (BMP9) induces effective bone formation from reversibly immortalized multipotent adipose-derived (iMAD) mesenchymal stem cells.

PubMed

Lu, Shun; Wang, Jing; Ye, Jixing; Zou, Yulong; Zhu, Yunxiao; Wei, Qiang; Wang, Xin; Tang, Shengli; Liu, Hao; Fan, Jiaming; Zhang, Fugui; Farina, Evan M; Mohammed, Maryam M; Song, Dongzhe; Liao, Junyi; Huang, Jiayi; Guo, Dan; Lu, Minpeng; Liu, Feng; Liu, Jianxiang; Li, Li; Ma, Chao; Hu, Xue; Lee, Michael J; Reid, Russell R; Ameer, Guillermo A; Zhou, Dongsheng; He, Tongchuan

2016-01-01

Regenerative medicine and bone tissue engineering using mesenchymal stem cells (MSCs) hold great promise as an effective approach to bone and skeletal reconstruction. While adipose tissue harbors MSC-like progenitors, or multipotent adipose-derived cells (MADs), it is important to identify and characterize potential biological factors that can effectively induce osteogenic differentiation of MADs. To overcome the time-consuming and technically challenging process of isolating and culturing primary MADs, here we establish and characterize the reversibly immortalized mouse multipotent adipose-derived cells (iMADs). The isolated mouse primary inguinal MAD cells are reversibly immortalized via the retrovirus-mediated expression of SV40 T antigen flanked with FRT sites. The iMADs are shown to express most common MSC markers. FLP-mediated removal of SV40 T antigen effectively reduces the proliferative activity and cell survival of iMADs, indicating the immortalization is reversible. Using the highly osteogenic BMP9, we find that the iMADs are highly responsive to BMP9 stimulation, express multiple lineage regulators, and undergo osteogenic differentiation in vitro upon BMP9 stimulation. Furthermore, we demonstrate that BMP9-stimulated iMADs form robust ectopic bone with a thermoresponsive biodegradable scaffold material. Collectively, our results demonstrate that the reversibly immortalized iMADs exhibit the characteristics of multipotent MSCs and are highly responsive to BMP9-induced osteogenic differentiation. Thus, the iMADs should provide a valuable resource for the study of MAD biology, which would ultimately enable us to develop novel and efficacious strategies for MAD-based bone tissue engineering.

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.

Improving the Post-Stroke Therapeutic Potency of Mesenchymal Multipotent Stromal Cells by Cocultivation With Cortical Neurons: The Role of Crosstalk Between Cells.

PubMed

Babenko, Valentina A; Silachev, Denis N; Zorova, Ljubava D; Pevzner, Irina B; Khutornenko, Anastasia A; Plotnikov, Egor Y; Sukhikh, Gennady T; Zorov, Dmitry B

2015-09-01

The goal of the present study was to maximally alleviate the negative impact of stroke by increasing the therapeutic potency of injected mesenchymal multipotent stromal cells (MMSCs). To pursue this goal, the intercellular communications of MMSCs and neuronal cells were studied in vitro. As a result of cocultivation of MMSCs and rat cortical neurons, we proved the existence of intercellular contacts providing transfer of cellular contents from one cell to another. We present evidence of intercellular exchange with fluorescent probes specifically occupied by cytosol with preferential transfer from neurons toward MMSCs. In contrast, we observed a reversed transfer of mitochondria (from MMSCs to neural cells). Intravenous injection of MMSCs in a postischemic period alleviated the pathological indexes of a stroke, expressed as a lower infarct volume in the brain and partial restoration of neurological status. Also, MMSCs after cocultivation with neurons demonstrated more profound neuroprotective effects than did unprimed MMSCs. The production of the brain-derived neurotrophic factor was slightly increased in MMSCs, and the factor itself was redistributed in these cells after cocultivation. The level of Miro1 responsible for intercellular traffic of mitochondria was increased in MMSCs after cocultivation. We conclude that the exchange by cellular compartments between neural and stem cells improves MMSCs' protective abilities for better rehabilitation after stroke. This could be used as an approach to enhance the therapeutic benefits of stem cell therapy to the damaged brain. The idea of priming stem cells before practical use for clinical purposes was applied. Thus, cells were preconditioned by coculturing them with the targeted cells (i.e., neurons for the treatment of brain pathological features) before the transfusion of stem cells to the organism. Such priming improved the capacity of stem cells to treat stroke. Some additional minimal study will be required to

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Zhu, Fujing; Huang, Jiaqi; Ma, Zhen; Zhang, Rui

2015-11-01

A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal learning parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Lifelong Bilingualism Maintains Neural Efficiency for Cognitive Control in Aging

PubMed Central

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, and 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 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 BOLD 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. PMID:23303919

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Neural-network hybrid control for antilock braking systems.

PubMed

Lin, Chih-Min; Hsu, C F

2003-01-01

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

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.

Generation of neural progenitor cells by chemical cocktails and hypoxia

PubMed Central

Cheng, Lin; Hu, Wenxiang; Qiu, Binlong; Zhao, Jian; Yu, Yongchun; Guan, Wuqiang; Wang, Min; Yang, Wuzhou; Pei, Gang

2014-01-01

Neural progenitor cells (NPCs) can be induced from somatic cells by defined factors. Here we report that NPCs can be generated from mouse embryonic fibroblasts by a chemical cocktail, namely VCR (V, VPA, an inhibitor of HDACs; C, CHIR99021, an inhibitor of GSK-3 kinases and R, Repsox, an inhibitor of TGF-β pathways), under a physiological hypoxic condition. These chemical-induced NPCs (ciNPCs) resemble mouse brain-derived NPCs regarding their proliferative and self-renewing abilities, gene expression profiles, and multipotency for different neuroectodermal lineages in vitro and in vivo. Further experiments reveal that alternative cocktails with inhibitors of histone deacetylation, glycogen synthase kinase, and TGF-β pathways show similar efficacies for ciNPC induction. Moreover, ciNPCs can also be induced from mouse tail-tip fibroblasts and human urinary cells with the same chemical cocktail VCR. Thus our study demonstrates that lineage-specific conversion of somatic cells to NPCs could be achieved by chemical cocktails without introducing exogenous factors. PMID:24638034

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.

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.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Neural Mechanisms Underlying Individual Differences in Control-Averse Behavior.

PubMed

Rudorf, Sarah; Schmelz, Katrin; Baumgartner, Thomas; Wiest, Roland; Fischbacher, Urs; Knoch, Daria

2018-05-30

When another person tries to control one's decisions, some people might comply, but many will feel the urge to act against that control. This control aversion can lead to suboptimal decisions and it affects social interactions in many societal domains. To date, however, it has been unclear what drives individual differences in control-averse behavior. Here, we address this issue by measuring brain activity with fMRI while healthy female and male human participants made choices that were either free or controlled by another person, with real consequences to both interaction partners. In addition, we assessed the participants' affects, social cognitions, and motivations via self-reports. Our results indicate that the social cognitions perceived distrust and lack of understanding for the other person play a key role in explaining control aversion at the behavioral level. At the neural level, we find that control-averse behavior can be explained by functional connectivity between the inferior parietal lobule and the dorsolateral prefrontal cortex, brain regions commonly associated with attention reorientation and cognitive control. Further analyses reveal that the individual strength of functional connectivity complements and partially mediates the self-reported social cognitions in explaining individual differences in control-averse behavior. These findings therefore provide valuable contributions to a more comprehensive model of control aversion. SIGNIFICANCE STATEMENT Control aversion is a prevalent phenomenon in our society. When someone tries to control their decisions, many people tend to act against the control. This can lead to suboptimal decisions such as noncompliance to medical treatments or disobeying the law. The degree to which individuals engage in control-averse behavior, however, varies significantly. Understanding the proximal mechanisms that underlie individual differences in control-averse behavior has potential policy implications, for example

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.

A model for the neural control of pineal periodicity

NASA Astrophysics Data System (ADS)

de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

2016-12-01

The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

Fibronectin promotes differentiation of neural crest progenitors endowed with smooth muscle cell potential

DOE Office of Scientific and Technical Information (OSTI.GOV)

Costa-Silva, Bruno; Programa de Pos-graduacao em Neurociencias, Centro de Ciencias Biologicas, Universidade Federal de Santa Catarina, Campus Universitario - Trindade, 88040-900, Florianopolis, S.C.; Coelho da Costa, Meline

The neural crest (NC) is a model system used to investigate multipotency during vertebrate development. Environmental factors control NC cell fate decisions. Despite the well-known influence of extracellular matrix molecules in NC cell migration, the issue of whether they also influence NC cell differentiation has not been addressed at the single cell level. By analyzing mass and clonal cultures of mouse cephalic and quail trunk NC cells, we show for the first time that fibronectin (FN) promotes differentiation into the smooth muscle cell phenotype without affecting differentiation into glia, neurons, and melanocytes. Time course analysis indicated that the FN-induced effectmore » was not related to massive cell death or proliferation of smooth muscle cells. Finally, by comparing clonal cultures of quail trunk NC cells grown on FN and collagen type IV (CLIV), we found that FN strongly increased both NC cell survival and the proportion of unipotent and oligopotent NC progenitors endowed with smooth muscle potential. In contrast, melanocytic progenitors were prominent in clonogenic NC cells grown on CLIV. Taken together, these results show that FN promotes NC cell differentiation along the smooth muscle lineage, and therefore plays an important role in fate decisions of NC progenitor cells.« less

Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, E.; Morgan, M. J.; Biedron, S. G.

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 themore » 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.« less

Expression of Pluripotency Markers in Nonpluripotent Human Neural Stem and Progenitor Cells.

PubMed

Vincent, Per Henrik; Benedikz, Eirikur; Uhlén, Per; Hovatta, Outi; Sundström, Erik

2017-06-15

Nonpluripotent neural progenitor cells (NPCs) derived from the human fetal central nervous system were found to express a number of messenger RNA (mRNA) species associated with pluripotency, such as NANOG, REX1, and OCT4. The expression was restricted to small subpopulations of NPCs. In contrast to pluripotent stem cells, there was no coexpression of the pluripotency-associated genes studied. Although the expression of these genes rapidly declined during the in vitro differentiation of NPCs, we found no evidence that the discrete expression was associated with the markers of multipotent neural stem cells (CD133 + /CD24 lo ), the capacity of sphere formation, or high cell proliferation rates. The rate of cell death among NPCs expressing pluripotency-associated genes was also similar to that of other NPCs. Live cell imaging showed that NANOG- and REX1-expressing NPCs continuously changed morphology, as did the nonexpressing cells. Depletion experiments showed that after the complete removal of the subpopulations of NANOG- and REX1-expressing NPCs, the expression of these genes appeared in other NPCs within a few days. The percentage of NANOG- and REX1-expressing cells returned to that observed before depletion. Our results are best explained by a model in which there is stochastic transient expression of pluripotency-associated genes in proliferating NPCs.

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.

Skeletal muscle-derived interstitial progenitor cells (PICs) display stem cell properties, being clonogenic, self-renewing, and multi-potent in vitro and in vivo.

PubMed

Cottle, Beverley J; Lewis, Fiona C; Shone, Victoria; Ellison-Hughes, Georgina M

2017-07-04

The development of cellular therapies to treat muscle wastage with disease or age is paramount. Resident muscle satellite cells are not currently regarded as a viable cell source due to their limited migration and growth capability ex vivo. This study investigated the potential of muscle-derived PW1 + /Pax7 - interstitial progenitor cells (PICs) as a source of tissue-specific stem/progenitor cells with stem cell properties and multipotency. Sca-1 + /PW1 + PICs were identified on tissue sections from hind limb muscle of 21-day-old mice, isolated by magnetic-activated cell sorting (MACS) technology and their phenotype and characteristics assessed over time in culture. Green fluorescent protein (GFP)-labelled PICs were used to determine multipotency in vivo in a tumour formation assay. Isolated PICs expressed markers of pluripotency (Oct3/4, Sox2, and Nanog), were clonogenic, and self-renewing with >60 population doublings, and a population doubling time of 15.8 ± 2.9 h. PICs demonstrated an ability to generate both striated and smooth muscle, whilst also displaying the potential to differentiate into cell types of the three germ layers both in vitro and in vivo. Moreover, PICs did not form tumours in vivo. These findings open new avenues for a variety of solid tissue engineering and regeneration approaches, utilising a single multipotent stem cell type isolated from an easily accessible source such as skeletal muscle.

Embryonic multipotent progenitors remodel the Drosophila airways during metamorphosis

PubMed Central

Pitsouli, Chrysoula; Perrimon, Norbert

2010-01-01

Adult structures in holometabolous insects such as Drosophila are generated by groups of imaginal cells dedicated to the formation of different organs. Imaginal cells are specified in the embryo and remain quiescent until the larval stages, when they proliferate and differentiate to form organs. The Drosophila tracheal system is extensively remodeled during metamorphosis by a small number of airway progenitors. Among these, the spiracular branch tracheoblasts are responsible for the generation of the pupal and adult abdominal airways. To understand the coordination of proliferation and differentiation during organogenesis of tubular organs, we analyzed the remodeling of Drosophila airways during metamorphosis. We show that the embryonic spiracular branch tracheoblasts are multipotent cells that express the homeobox transcription factor Cut, which is necessary for their survival and normal development. They give rise to three distinct cell populations at the end of larval development, which generate the adult tracheal tubes, the spiracle and the epidermis surrounding the spiracle. Our study establishes the series of events that lead to the formation of an adult tubular structure in Drosophila. PMID:20940225

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

PubMed

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

H∞ output tracking control of discrete-time nonlinear systems via standard neural network models.

PubMed

Liu, Meiqin; Zhang, Senlin; Chen, Haiyang; Sheng, Weihua

2014-10-01

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

PubMed

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

PubMed Central

Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

Bilingualism increases neural response consistency and attentional control: Evidence for sensory and cognitive coupling

PubMed Central

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] and separately obtained measures of attentional control and language ability in adolescent Spanish-English bilinguals and English monolinguals. 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. PMID:24413593

Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment.

PubMed

da Fonseca Neto, João Viana; Abreu, Ivanildo Silva; da Silva, Fábio Nogueira

2010-04-01

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

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

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

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

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

PubMed

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition

PubMed Central

Barriga, Elias H.; Maxwell, Patrick H.

2013-01-01

One of the most important mechanisms that promotes metastasis is the stabilization of Hif-1 (hypoxia-inducible transcription factor 1). We decided to test whether Hif-1α also was required for early embryonic development. We focused our attention on the development of the neural crest, a highly migratory embryonic cell population whose behavior has been likened to cancer metastasis. Inhibition of Hif-1α by antisense morpholinos in Xenopus laevis or zebrafish embryos led to complete inhibition of neural crest migration. We show that Hif-1α controls the expression of Twist, which in turn represses E-cadherin during epithelial to mesenchymal transition (EMT) of neural crest cells. Thus, Hif-1α allows cells to initiate migration by promoting the release of cell–cell adhesions. Additionally, Hif-1α controls chemotaxis toward the chemokine SDF-1 by regulating expression of its receptor Cxcr4. Our results point to Hif-1α as a novel and key regulator that integrates EMT and chemotaxis during migration of neural crest cells. PMID:23712262

A new learning strategy for the two-time-scale neural controller with its application to the tracking control of rigid arms

NASA Technical Reports Server (NTRS)

Cheng, W.; Wen, J. T.

1992-01-01

A novel fast learning rule with fast weight identification is proposed for the two-time-scale neural controller, and a two-stage learning strategy is developed for the proposed neural controller. The results of the stability analysis show that both the tracking error and the fast weight error will be uniformly bounded and converge to a bounded region which depends only on the accuracy of the slow learning if the system is sufficiently excited. The efficiency of the two-stage learning is also demonstrated by a simulation of a two-link arm.

Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

PubMed

Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

2004-01-01

This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

Neural-Learning-Based Telerobot Control With Guaranteed Performance.

PubMed

Yang, Chenguang; Wang, Xinyu; Cheng, Long; Ma, Hongbin

2017-10-01

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

PubMed Central

Li, YuHui; Jin, FeiTeng

2017-01-01

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller. PMID:29410680

Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

PubMed

Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

2014-03-15

The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

A role for chemokine signaling in neural crest cell migration and craniofacial development

PubMed Central

Killian, Eugenia C. Olesnicky; Birkholz, Denise A.; Artinger, Kristin Bruk

2009-01-01

Neural crest cells (NCCs) are a unique population of multipotent cells that migrate along defined pathways throughout the embryo and give rise to many diverse cell types including pigment cells, craniofacial cartilage and the peripheral nervous system (PNS). Aberrant migration of NCCs results in a wide variety of congenital birth defects including craniofacial abnormalities. The chemokine Sdf1 and its receptors, Cxcr4 and Cxcr7, have been identified as key components in the regulation of cell migration in a variety of tissues. Here we describe a novel role for the zebrafish chemokine receptor Cxcr4a in the development and migration of cranial NCCs (CNCCs). We find that loss of Cxcr4a, but not Cxcr7b results in aberrant CNCC migration, defects in the neurocranium, as well as cranial ganglia dismorphogenesis. Moreover, overexpression of either Sdf1b or Cxcr4a causes aberrant CNCC migration and results in ectopic craniofacial cartilages. We propose a model in which Sdf1b signaling from the pharyngeal arch endoderm and optic stalk to Cxcr4a expressing CNCCs is important for both the proper condensation of the CNCCs into pharyngeal arches and the subsequent patterning and morphogenesis of the neural crest derived tissues. PMID:19576198

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.

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

NASA Astrophysics Data System (ADS)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Telomerase-immortalized non-malignant human prostate epithelial cells retain the properties of multipotent stem cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Hongzhen; Zhou Jianjun; Miki, Jun

2008-01-01

Understanding prostate stem cells may provide insight into the origin of prostate cancer. Primary cells have been cultured from human prostate tissue but they usually survive only 15-20 population doublings before undergoing senescence. We report here that RC-170N/h/clone 7 cells, a clonal cell line from hTERT-immortalized primary non-malignant tissue-derived human prostate epithelial cell line (RC170N/h), retain multipotent stem cell properties. The RC-170N/h/clone 7 cells expressed a human embryonic stem cell marker, Oct-4, and potential prostate epithelial stem cell markers, CD133, integrin {alpha}2{beta}1{sup hi} and CD44. The RC-170N/h/clone 7 cells proliferated in KGM and Dulbecco's Modified Eagle Medium with 10% fetalmore » bovine serum and 5 {mu}g/ml insulin (DMEM + 10% FBS + Ins.) medium, and differentiated into epithelial stem cells that expressed epithelial cell markers, including CK5/14, CD44, p63 and cytokeratin 18 (CK18); as well as the mesenchymal cell markers, vimentin, desmin; the neuron and neuroendocrine cell marker, chromogranin A. Furthermore the RC170 N/h/clone 7 cells differentiated into multi tissues when transplanted into the sub-renal capsule and subcutaneously of NOD-SCID mice. The results indicate that RC170N/h/clone 7 cells retain the properties of multipotent stem cells and will be useful as a novel cell model for studying the mechanisms of human prostate stem cell differentiation and transformation.« less

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

PubMed

Liu, Zhi; Lai, Guanyu; Zhang, Yun; Chen, Xin; Chen, Chun Lung Philip

2014-12-01

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

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.

Accelerator diagnosis and control by Neural Nets

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 tomore » '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.« less

Injectable polypeptide hydrogels via methionine modification for neural stem cell delivery.

PubMed

Wollenberg, A L; O'Shea, T M; Kim, J H; Czechanski, A; Reinholdt, L G; Sofroniew, M V; Deming, T J

2018-04-05

Injectable hydrogels with tunable physiochemical and biological properties are potential tools for improving neural stem/progenitor cell (NSPC) transplantation to treat central nervous system (CNS) injury and disease. Here, we developed injectable diblock copolypeptide hydrogels (DCH) for NSPC transplantation that contain hydrophilic segments of modified l-methionine (Met). Multiple Met-based DCH were fabricated by post-polymerization modification of Met to various functional derivatives, and incorporation of different amino acid comonomers into hydrophilic segments. Met-based DCH assembled into self-healing hydrogels with concentration and composition dependent mechanical properties. Mechanical properties of non-ionic Met-sulfoxide formulations (DCH MO ) were stable across diverse aqueous media while cationic formulations showed salt ion dependent stiffness reduction. Murine NSPC survival in DCH MO was equivalent to that of standard culture conditions, and sulfoxide functionality imparted cell non-fouling character. Within serum rich environments in vitro, DCH MO was superior at preserving NSPC stemness and multipotency compared to cell adhesive materials. NSPC in DCH MO injected into uninjured forebrain remained local and, after 4 weeks, exhibited an immature astroglial phenotype that integrated with host neural tissue and acted as cellular substrates that supported growth of host-derived axons. These findings demonstrate that Met-based DCH are suitable vehicles for further study of NSPC transplantation in CNS injury and disease models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Controlling basins of attraction in a neural network-based telemetry monitor

NASA Technical Reports Server (NTRS)

Bell, Benjamin; Eilbert, James L.