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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Serotonin release varies with brain tryptophan levels

This study examines directly the effects on serotonin release of varying brain tryptophan levels within the physiologic range. It also addresses possible interactions between tryptophan availability and the frequency of membrane depolarization in controlling serotonin release. We demonstrate that reducing tryptophan levels in rat hypothalamic slices (by superfusing them with medium supplemented with 100 microM leucine) decreases tissue serotonin levels as well as both the spontaneous and the electrically-evoked serotonin release. Conversely, elevating tissue tryptophan levels (by superfusing slices with medium supplemented with 2 microM tryptophan) increases both the tissue serotonin levels and the serotonin release. Serotonin release was found to be affected independently by the tryptophan availability and the frequency of electrical field-stimulation (1-5 Hz), since increasing both variables produced nearly additive increases in release. These observations demonstrate for the first time that both precursor-dependent elevations and reductions in brain serotonin levels produce proportionate changes in serotonin release, and that the magnitude of the tryptophan effect is unrelated to neuronal firing frequency. The data support the hypothesis that serotonin release is proportionate to intracellular serotonin levels.

Schaechter, Judith D.↗
NASA NTRS1988

Tyrosine - Effects on catecholamine release

Tyrosine administration elevates striatal levels of dopamine metabolites in animals given treatments that accelerate nigrostriatal firing, but not in untreated rats. We examined the possibility that the amino acid might actually enhance dopamine release in untreated animals, but that the technique of measuring striatal dopamine metabolism was too insensitive to demonstrate such an effect. Dopamine release was assessed directly, using brain microdialysis of striatal extracellular fluid. Tyrosine administration (50-200 mg/kg IP) did indeed cause a dose related increase in extracellular fluid dopamine levels with minor elevations in levels of DOPAC and HVA, its major metabolites, which were not dose-related. The rise in dopamine was short-lived, suggesting that receptor-mediated feedback mechanisms responded to the increased dopamine release by diminishing neuronal firing or sensitivity to tyrosine. These observations indicate that measurement of changes in striatal DOPAC and HVA, if negative, need not rule out increases in nigrostriatal dopamine release.

Acworth, Ian N.↗
NASA NTRS1992

Optical implementation of terminal-attractor-based associative memory

For the purpose of reducing the spurious states in a Hopfield neural net for associative memory, we invoke terminal attractors. In achieving the optical implementation of the terminal-attractor-based associative memory (TABAM) as described in this paper, we first prove the existence of the terminal-attractor model with binary neuron representation. We then present several one- and two-dimensional optical architectures for the TABAM. Finally, as an example, we experimentally demonstrate an inner-product optical neural model using liquid-crystal spatial light modulators with engineering approximations and discuss how to apply the inner-product model to build a two-dimensional parallel processing TABAM.

Liu, Hua-Kuang↗
NASA NTRS1993

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Salu, Yehuda↗
NASA NTRS1988

Effects of hemorrhagic hypotension on tyrosine concentrations in rat spinal cord and plasma

Tyrosine is the precursor for catecholamine neurotransmitters. When catecholamine-containing neurons are physiologically active (as sympathoadrenal cells are in hypotension), tyrosine administration increases catecholamine synthesis and release. Since hypotension can alter plasma amino acid composition, the effects of an acute hypotensive insult on tyrosine concentrations in plasma and spinal cord were examined. Rats were cannulated and bled until the systolic blood pressure was 50 mmHg, or were kept normotensive for 1 h. Tyrosine and other large neutral amino acids (LNAA) known to compete with tyrosine for brain uptake were assayed in plasma and spinal cord. The rate at which intra-arterial (H-3)tyrosine disappeared from the plasma was also estimated in hemorrhaged and control rats. In plasma of hemorrhaged animals, both the tyrosine concentration and the tyrosine/LNAA ratio was elevated; moreover, the disappearance of (H-3)tyrosine was slowed. Tyrosine concentrations also increased in spinal cords of hemorrhaged-hypotensive rats when compared to normotensive controls. Changes in plasma amino acid patterns may thus influence spinal cord concentrations of amino acid precursors for neurotransmitters during the stress of hemorrhagic shock.

Conlay, L. A.↗
NASA NTRS1986

Meal composition and plasma amino acid ratios: Effect of various proteins or carbohydrates, and of various protein concentrations

The effects of meals containing various proteins and carbohydrates, and of those containing various proportions of protein (0 percent to 20 percent of a meal, by weight) or of carbohydrate (0 percent to 75 percent), on plasma levels of certain large neutral amino acids (LNAA) in rats previously fasted for 19 hours were examined. Also the plasma tryptophan ratios (the ratio of the plasma trytophan concentration to the summed concentrations of the other large neutral amino acids) and other plasma amino acid ratios were calculated. (The plasma tryptophan ratio has been shown to determine brain tryptophan levels and, thereby, to affect the synthesis and release of the neurotransmitter serotonin). A meal containing 70 percent to 75 percent of an insulin-secreting carbohydrate (dextrose or dextrin) increased plasma insulin levels and the tryptophan ratio; those containing 0 percent or 25 percent carbohydrate failed to do so. Addition of as little as 5 percent casein to a 70 percent carbohydrate meal fully blocked the increase in the plasma tryptophan ratio without affecting the secretion of insulin - probably by contributing much larger quantities of the other LNAA than of tryptophan to the blood. Dietary proteins differed in their ability to suppress the carbohydrate-induced rise in the plasma tryptophan ratio. Addition of 10 percent casein, peanut meal, or gelatin fully blocked this increase, but lactalbumin failed to do so, and egg white did so only partially. (Consumption of the 10 percent gelatin meal also produced a major reduction in the plasma tyrosine ratio, and may thereby have affected brain tyrosine levels and catecholamine synthesis.) These observations suggest that serotonin-releasing neurons in brains of fasted rats are capable of distinguishing (by their metabolic effects) between meals poor in protein but rich in carbohydrates that elicit insulin secretion, and all other meals. The changes in brain serotonin caused by carbohydrate-rich, protein-poor meals may affect subsequent food choice and various serotonin-mediated behaviors.

Yokogoshi, Hidehiko↗
NASA NTRS1993

Communications and control for electric power systems: Power flow classification for static security assessment

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Niebur, D.↗
NASA NTRS1992

Strategies for enhancing catecholamine-mediated neurotransmission

Major findings made during this project period included the following observations: changes in tyrosine availability do affect brain dopamine release, as assessed by in vivo microdialysis, but that neuronal feedback mechanisms limit the durations of this effect except when dopaminergic neurotransmission has been deficient; the circulating hormone TRH markedly stimulates brain dopamine release, an effect probably mediated by its diketopiperazine metabolite; the amount of circulating L-dopa which enters the brain is both enhanced by carbohydrate consumption and suppressed by protein intake (both nutritional effects can be damaging, inasmuch as a sudden rush of L-dopa into the brain can facilitate dyskinesias, while the inhibition of brain L-dopa uptake by proteins suppresses its conversion to brain dopamine; an appropriate mixture of dietary proteins and carbohydrates can obviate both effects); serotonin release from superfused hypothalamic slices is a linear function of available tryptophan levels throughout the normal dynamic range; the daily rhythm in plasma melatonin levels is abnormal both in the sudden infant death syndrome and in women with secondary amenorrhea; tyrosine can potentiate the anorectic effects of widely-used sympathomimetic drugs; newly-described COMT inhibitors can enhance brain dopamine release in vivo; and a cell culture system, based on Y-79 (retinoblast) cells, exists in which melatonin reliably suppresses dopamine release.

Wurtman, Richard J.↗
NASA NTRS1993

Neural network for processing both spatial and temporal data with time based back-propagation

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Villarreal, James A.↗
NASA NTRS1988

Immunocytochemical localization of glutamic acid decarboxylase (GAD) and substance P in neural areas mediating motion-induced emesis: Effects of vagal stimulation on GAD immunoreactivity

Immunocytochemical methods were employed to localize the neurotransmitter amino acid gamma-aminobutyric acid (GABA) by means of its biosynthetic enzyme glutamic acid decarboxylase (GAD) and the neuropeptide substance P in the area postrema (AP), area subpostrema (ASP), nucleus of the tractus solitarius (NTS), and gelatinous nucleus (GEL). In addition, electrical stimulation was applied to the night vagus nerve at the cervical level to assess the effects on GAD-immunoreactivity (GAR-IR). GAD-IR terminals and fibers were observed in the AP, ASP, NTS, and GEL. They showed pronounced density at the level of the ASP and gradual decrease towards the solitary complex. Nerve cells were not labelled in our preparations. Ultrastructural studies showed symmetric or asymmetric synaptic contracts between labelled terminals and non-immunoreactive dendrites, axons, or neurons. Some of the labelled terminals contained both clear- and dense-core vesicles. Our preliminary findings, after electrical stimulation of the vagus nerve, revealed a bilateral decrease of GAD-IR that was particularly evident at the level of the ASP. SP-immunoreactive (SP-IR) terminals and fibers showed varying densities in the AP, ASP, NTS, and GEL. In our preparations, the lateral sub-division of the NTS showed the greatest accumulation. The ASP showed medium density of immunoreactive varicosities and terminals and the AP and GEL displayed scattered varicose axon terminals. The electron microscopy revealed that all immunoreactive terminals contained clear-core vesicles which make symmetric or asymmetric synaptic contact with unlabelled dendrites. It is suggested that the GABAergic terminals might correspond to vagal afferent projections and that GAD/GABA and substance P might be co-localized in the same terminal allowing the possibility of a regulated release of the transmitters in relation to demands.

GABA↗
NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗
NASA NTRS1993

Neural nets with terminal chaos for simulation of non-deterministic patterns

Models for simulating some aspects of neural intelligence are presented and discussed. Special attention is given to terminal neurodynamics as a particular architecture of terminal dynamics suitable for modeling information flows. Applications of terminal chaos to information fusion as well as to planning and modeling coordination among neurons in biological systems are disussed.

Zak, Michail↗
NASA NTRS1994

Neuritogenesis: A model for space radiation effects on the central nervous system

Pivotal to the astronauts' functional integrity and survival during long space flights are the strategies to deal with space radiations. The majority of the cellular studies in this area emphasize simple endpoints such as growth related events which, although useful to understand the nature of primary cell injury, have poor predictive value for extrapolation to more complex tissues such as the central nervous system (CNS). In order to assess the radiation damage on neural cell populations, we developed an in vitro model in which neuronal differentiation, neurite extension, and synaptogenesis occur under controlled conditions. The model exploits chick embryo neural explants to study the effects of radiations on neuritogenesis. In addition, neurobiological problems associated with long-term space flights are discussed.

Vazquez, M. E.↗
NASA NTRS1994

A mission planning concept and mission planning system for future manned space missions

The international character of future manned space missions will compel the involvement of several international space agencies in mission planning tasks. Additionally, the community of users requires a higher degree of freedom for experiment planning. Both of these problems can be solved by a decentralized mission planning concept using the so-called 'envelope method,' by which resources are allocated to users by distributing resource profiles ('envelopes') which define resource availabilities at specified times. The users are essentially free to plan their activities independently of each other, provided that they stay within their envelopes. The new developments were aimed at refining the existing vague envelope concept into a practical method for decentralized planning. Selected critical functions were exercised by planning an example, founded on experience acquired by the MSCC during the Spacelab missions D-1 and D-2. The main activity regarding future mission planning tasks was to improve the existing MSCC mission planning system, using new techniques. An electronic interface was developed to collect all formalized user inputs more effectively, along with an 'envelope generator' for generation and manipulation of the resource envelopes. The existing scheduler and its data base were successfully replaced by an artificial intelligence scheduler. This scheduler is not only capable of handling resource envelopes, but also uses a new technology based on neuronal networks. Therefore, it is very well suited to solve the future scheduling problems more efficiently. This prototype mission planning system was used to gain new practical experience with decentralized mission planning, using the envelope method. In future steps, software tools will be optimized, and all data management planning activities will be embedded into the scheduler.

Wickler, Martin↗
NASA NTRS1994

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

Kong, Jeffrey↗
NASA NTRS1995

Lyapunov exponents from CHUA's circuit time series using artificial neural networks

In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

Gonzalez, J. Jesus↗
NASA NTRS1994

On-line, adaptive state estimator for active noise control

Dynamic characteristics of airframe structures are expected to vary as aircraft flight conditions change. Accurate knowledge of the changing dynamic characteristics is crucial to enhancing the performance of the active noise control system using feedback control. This research investigates the development of an adaptive, on-line state estimator using a neural network concept to conduct active noise control. In this research, an algorithm has been developed that can be used to estimate displacement and velocity responses at any locations on the structure from a limited number of acceleration measurements and input force information. The algorithm employs band-pass filters to extract from the measurement signal the frequency contents corresponding to a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network is designed as simple as possible to increase the sampling frequency as much as possible. The weights obtained through neural network training are then used to construct the transfer function of a mode in z-domain and to identify modal properties of each mode. By using the identified transfer function and interpolating the mode shape obtained at sensor locations, the displacement and velocity responses are estimated with reasonable accuracy at any locations on the structure. The accuracy of the response estimates depends on the number of modes incorporated in the estimates and the number of sensors employed to conduct mode shape interpolation. Computer simulation demonstrates that the algorithm is capable of adapting to the varying dynamic characteristics of structural properties. Experimental implementation of the algorithm on a DSP (digital signal processing) board for a plate structure is underway. The algorithm is expected to reach the sampling frequency range of about 10 kHz to 20 kHz which needs to be maintained for a typical active noise control application.

Lim, Tae W.↗
NASA NTRS1993

The 1993 Gordon Research Conference on Chronobiology

The study of biological timekeeping is now at a particularly fertile stage, encompassing multiple levels of biological organization, recruiting a wide array of disciplines and methodologies and uniting a host of investigators. This report summarizes a research conference on Chronobiology. Some of the topics focused on transcriptional and translational mechanisms of circadian rhythmicity, with discussions of putative 'clock genes' in cyanobacteria, algae, fungi, fruitflies, and hamsters. Cellular analysis, with emphasis on photoreceptors in frogs, neurons in molluscs, and testis in moths was addressed. New methods for investigating the circadian clock in the suprachiasmatic nucleus were introduced.

Schwartz, William J.↗