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NASA NTRS1992

Learning and optimization with cascaded VLSI neural network building-block chips

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.

Duong, T.↗

NASA NTRS1987

A neurocomputer based on an analog-digital hybrid architecture

A novel analog-digital hybrid architecture based on the utilization of high density digital random access memories for the storage of the synaptic weights of a neural network, and high speed analog hardware to perform neural computation is described. An electronic neurocomputer based on such an architecture is ideally suited for investigating the dynamics, associative recall properties, and computational capabilities of neural networks and provides significant speed improvement in comparison to conventional software based neural network simulations. As a demonstration of the feasibility of the hybrid architectural concept, a prototype breadboard hybrid neurocomputer system with 32 neurons has been designed and fabricated with off-the-shelf hardware components. The performance of the breadboard system has been tested for variety of applications including associative memory and combinatorial problem solving such as Graph Coloring, and is discussed in this paper.

Moopenn, A.↗

NASA NTRS1989

Sensory conflict in motion sickness: An observer theory approach

Motion sickness is the general term describing a group of common nausea syndromes originally attributed to motion-induced cerebral ischemia, stimulation of abdominal organ afferent, or overstimulation of the vestibular organs of the inner ear. Sea-, car-, and airsicknesses are the most commonly experienced examples. However, the discovery of other variants such as Cinerama-, flight simulator-, spectacle-, and space sickness in which the physical motion of the head and body is normal or absent has led to a succession of sensory conflict theories which offer a more comprehensive etiologic perspective. Implicit in the conflict theory is the hypothesis that neutral and/or humoral signals originate in regions of the brain subversing spatial orientation, and that these signals somehow traverse to other centers mediating sickness symptoms. Unfortunately, the present understanding of the neurophysiological basis of motion sickness is far from complete. No sensory conflict neuron or process has yet been physiologically identified. To what extent can the existing theory be reconciled with current knowledge of the physiology and pharmacology of nausea and vomiting. The stimuli which causes sickness, synthesizes a contemporary Observer Theory view of the Sensory Conflict hypothesis are reviewed, and a revised model for the dynamic coupling between the putative conflict signals and nausea magnitude estimates is presented. The use of quantitative models for sensory conflict offers a possible new approach to improving the design of visual and motion systems for flight simulators and other virtual environment display systems.

Oman, Charles M.↗

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 NTRS2004

Complexity of life via collective mind

e mind is introduced as a set of simple intelligent units (say, neurons, or interacting agents), which can communicate by exchange of information without explicit global control. Incomplete information is compensated by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute into dynamical formalism of the agents' evolution, and in particular, into the reflective chains of their nested abstract images of the selves and non-selves. The proposed model consists of the system of stochastic differential equations in the Langevin form representing the motor dynamics, and the corresponding Fokker-Planck equation representing the mental dynamics (Motor dynamics describes the motion in physical space, while mental dynamics simulates the evolution of initial errors in terms of the probability density). The main departure of this model from Newtonian and statistical physics is due to a feedback from the mental to the motor dynamics which makes the Fokker-Planck equation nonlinear. Interpretation of this model from mathematical and physical viewpoints, as well as possible interpretation from biological, psychological, and social viewpoints are discussed. The model is illustrated by the dynamics of a dialog.

collective intelligence↗

NASA NTRS1992

Effects of spaceflight on hypothalamic peptide systems controlling pituitary growth hormone dynamics

Possible effects of reduced gravity on central hypophysiotropic systems controlling growth hormone (GH) secretion were investigated in rats flown on Cosmos 1887 and 2044 biosatellites. Immunohistochemical (IHC)staining for the growth hormone-releasing factor (GRF), somatostatin (SS), and other hypothalamic hormones was performed on hypothalami obtained from rats. IHC analysis was complemented by quantitative in situ assessments of mRNAs encoding the precursors for these hormones. Data obtained suggest that exposure to microgravity causes a preferential reduction in GRF peptide and mRNA levels in hypophysiotropic neurons, which may contribute to impared GH secretion in animals subjected to spaceflight. Effects of weightlessness are not mimicked by hindlimb suspension in this system.

Sawchenko, P. E.↗

NASA NTRS1980

Effect of restricted mobility on RNA content and nucleotide composition and on protein content in motoneurons of spinal cord anterior horns

An investigation into the effect of hypokinesia on the ribonucleic acid (RNA) content, the nucleotide composition, and dynamics of protein content in the motoneuron of the rat spinal cord anterior horns is described. Methodology and findings are presented. The study results showed that the nucleotide composition of the total cellular RNA at all the studied periods of hypokinesia remained unchanged and is characteristic for the cytoplasmic, high polymer ribosomal RNA. This means that with a change in the functional state of the neuron the newly formed RNA of the nerve cell has the same composition of bases as the original RNA that belongs to the ribosomal type.

Gorbunova, A. V.↗

NASA NTRS2019

Vestibulo-Spinal Pathways in Tetrapods: Chapter - 6.26

The vestibulospinal system provides the spinal motor circuits controlling head/neck and limb movements and body posture with rapid reflex adjustments to maintain equilibrium and stability and with a continuous essential excitatory drive, called tonus, to enhance reactive responses to perturbations that force the animal off normal posture. The sensory signals to these reflex circuits originate from hair cells in the inner ear of otolith structures, namely the utricle and saccule, that transduce inertial acceleration and orientation of the head with respect to gravity and in the three orthogonally arranged semicircular canals that transduce angular head rotation. The principal vestibulospinal pathways are 1) the medial vestibulospinal tract that descends in the ventromedial funiculus and innervates inter- and motoneurons located mainly in lamina VII, VIII, and dorsomedial IX throughout the cervical segments; and 2) the lateral vestibulospinal tracts that course in the lateral to ventrolateral funiculi and are distinguished by two divisions: i) a cervical-projecting tract that overlaps many of the targets of medial vestibulospinal tract neurons including the motoneurons in ventromedial IX and also contributes to reflex control of shoulder and forelimb (arm) muscles; and ii) a lumbosacral-projecting tract that provides a rapid input to maintain stable posture and reflex control of the lower body. A striking observation in understanding the functional organization of this sensory-motor system is both that the driving sensory input can be dynamically modified by the behavioral context in which the sensation is made and that it remains able to quickly respond to an external force during self-generated head movements. The structural basis for vestibulospinal inputs to spinal motor control circuits in quadrupeds and bipeds rely in part on the animal's need for coordination between fore- and hind-limb reflex movements. Understanding the sensory-to-motor transformations in the diverse species rely on the correlations of the conserved and unique species behavior, morphology and physiologic function.

Spinal Cord↗

NASA NTRS1997

Response properties of pigeon otolith afferents to linear acceleration

In the present study, the sensitivity to sinusoidal linear accelerations in the plane of the utricular macula was tested in afferents. The head orientation relative to the translation axis was varied in order to determine the head position that elicited the maximal and minimal responses for each afferent. The response gain and phase values obtained to 0.5-Hz and 2-Hz linear acceleration stimuli were then plotted as a function of head orientation and a modified cosine function was fit to the data. From the best-fit cosine function, the predicted head orientations that would produce the maximal and minimal response gains were estimated. The estimated maximum response gains to linear acceleration in the utricular plane for the afferents varied between 75 and 1420 spikes s-1 g-1. The mean maximal gains for all afferents to 0.5-Hz and 2-Hz sinusoidal linear acceleration stimuli were 282 and 367 spikes s-1 g-1, respectively. The minimal response gains were essentially zero for most units. The response phases always led linear acceleration and remained constant for each afferent, regardless of head orientation. These response characteristics indicate that otolith afferents are cosine tuned and behave as one-dimensional linear accelerometers. The directions of maximal sensitivity to linear acceleration for the afferents varied throughout the plane of the utricle; however, most vectors were directed out of the opposite ear near the interaural axis. The response dynamics of the afferents were tested using stimulus frequencies ranging between 0.25 Hz and 10 Hz (0.1 g peak acceleration). Across stimulus frequencies, most afferents had increasing gains and constant phase values. These dynamic properties for individual afferents were fit with a simple transfer function that included three parameters: a mechanical time constant, a gain constant, and a fractional order distributed adaptation operator.

Non-NASA Center↗

NASA NTRS1990

Programmable synaptic devices for electronic neural nets

The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

Moopenn, A.↗

NASA NTRS1992

Role of irregular otolith afferents in the steady-state nystagmus during off-vertical axis rotation

1. During constant velocity off-vertical axis rotations (OVAR) in the dark a compensatory ocular nystagmus is present throughout rotation despite the lack of a maintained signal from the semicircular canals. Lesion experiments and canal plugging have attributed the steady-state ocular nystagmus during OVAR to inputs from the otolith organs and have demonstrated that it depends on an intact velocity storage mechanism. 2. To test whether irregularly discharging otolith afferents play a crucial role in the generation of the steady-state eye nystagmus during OVAR, we have used anodal (inhibitory) currents bilaterally to selectively and reversibly block irregular vestibular afferent discharge. During delivery of DC anodal currents (100 microA) bilaterally to both ears, the slow phase eye velocity of the steady-state nystagmus during OVAR was reduced or completely abolished. The disruption of the steady-state nystagmus was transient and lasted only during the period of galvanic stimulation. 3. To distinguish a possible effect of ablation of the background discharge rates of irregular vestibular afferents on the velocity storage mechanism from specific contributions of the dynamic responses from irregular otolith afferents to the circuit responsible for the generation of the steady-state nystagmus, bilateral DC anodal galvanic stimulation was applied during optokinetic nystagmus (OKN) and optokinetic afternystagmus (OKAN). No change in OKN and OKAN was observed.(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Discipline Neuroscience↗

NASA NTRS2000

Simulation studies of vestibular macular afferent-discharge patterns using a new, quasi-3-D finite volume method

A quasi-three-dimensional finite-volume numerical simulator was developed to study passive voltage spread in vestibular macular afferents. The method, borrowed from computational fluid dynamics, discretizes events transpiring in small volumes over time. The afferent simulated had three calyces with processes. The number of processes and synapses, and direction and timing of synapse activation, were varied. Simultaneous synapse activation resulted in shortest latency, while directional activation (proximal to distal and distal to proximal) yielded most regular discharges. Color-coded visualizations showed that the simulator discretized events and demonstrated that discharge produced a distal spread of voltage from the spike initiator into the ending. The simulations indicate that directional input, morphology, and timing of synapse activation can affect discharge properties, as must also distal spread of voltage from the spike initiator. The finite volume method has generality and can be applied to more complex neurons to explore discrete synaptic effects in four dimensions.

NASA Center ARC↗

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 NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

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.

Jorgensen, Charles C.↗

NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Harrivel, Angela↗

NASA NTRS2004

Postflight balance control recovery in an elderly astronaut: a case report

OBJECTIVE: To examine the sensorimotor adaptive response of a 77-year-old man exposed to the gravito-inertial challenges of orbital space flight. STUDY DESIGN: Prospective case study with retrospective comparisons. SETTING: NASA Neurosciences Laboratory (Johnson Space Center) and Baseline Data Collection Facility (Kennedy Space Center). PRIMARY PARTICIPANT: One 77-year-old male shuttle astronaut. INTERVENTION: Insertion into low Earth orbit was used to remove gravitational stimuli and thereby trigger sensorimotor adaptation to the microgravity environment. Graviceptor stimulation was reintroduced at landing, and sensorimotor readaptation to the terrestrial environment was tracked to completion. MAIN OUTCOME MEASURES: Computerized dynamic posturography tests were administered before and after orbital flight to determine the magnitude and time course of recovery. RESULTS: The elderly astronaut exhibited balance control performance decrements on landing day; however, there were no significant differences between his performance and that of younger astronauts tested on the same shuttle mission or on previous shuttle missions of similar duration. CONCLUSIONS: These results demonstrate that the physiological changes attributed to aging do not necessarily impair adaptive sensorimotor control processes.

Case Reports↗