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NASA NTRS1989

Terminal attractors in neural networks

A new type of attractor (terminal attractors) for content-addressable memory, associative memory, and pattern recognition in artificial neural networks operating in continuous time is introduced. The idea of a terminal attractor is based upon a violation of the Lipschitz condition at a fixed point. As a result, the fixed point becomes a singular solution which envelopes the family of regular solutions, while each regular solution approaches such an attractor in finite time. It will be shown that terminal attractors can be incorporated into neural networks such that any desired set of these attractors with prescribed basins is provided by an appropriate selection of the synaptic weights. The applications of terminal attractors for content-addressable and associative memories, pattern recognition, self-organization, and for dynamical training are illustrated.

Zak, Michail↗

NASA NTRS1994

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system also achieved a high accuracy of picking signals which represent the onset of VT, that is, VT immediately followed these signals. A special binary representation of the input and output data allowed the neural network to train very rapidly as compared to another standard decimal or normalized representations of the data.

Moebes, T. A.↗

NASA NTRS1993

Anomaly detection of turbopump vibration in Space Shuttle Main Engine using statistics and neural networks

The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.

Lo, C. F.↗

NASA NTRS1996

Studies of Vestibular Neurons in Normal, Hyper- and Hypogravity

During the past year, pre-, in- and postflight studies were conducted in association with the Axon project for Bion 10 (Cosmos 2229). Recordings were made during pre- and postflight studies, from 118 horizontal semicircular canal afferents and 27 vestibular nucleus neurons in 7 rhesus monkeys; 137 pulse rotation protocols alone were executed (548 acceleration and deceleration responses were curve fit). Usable data was obtained from 127 horizontal afferents concerning their spontaneous discharge. Curve fits and analysis was made of sinusoidal and sum of sinusoidal responses from 42 and 35 horizontal afferents, respectively. Also recordings were made from neurons inflight from the two flight animals. The mean spontaneous rate varied from 128 spikes/sec. during preflight to 92 spikes/sec during postflight (day 5) - a change of 28%. In direct contrast to the results of Cosmos 2044, the best fitted neural adaptation operator (k) and the gain of the pulse response were decreased during post flight when compared to preflight. Surprisingly, the best fitted gain and k values for the sum of sines were slightly elevated during post flight tests. The gain and phase of single sine responses were compared for pre- and post flight tests and compared to a larger population of afferents. In contrast to Cosmos 2044 results where on the first day of post flight testing the gains of the best fitted sine response were skewed toward the higher values of the Miles and Braitman distribution, the gain of the best fitted sine responses during the first day of post flight testing (day 2) during Cosmos 2229 were exactly on the mode of the Miles and Braitman distribution. Thus, at least for the periodic stimuli, (pulses and sine waves) we found no change in gain and neural adaptation during post flight testing following Cosmos 2229. This conclusion is different from the one derived following the Cosmos 2044 flight. Cosmos flight 2229 differed from Cosmos flight 2044 in several significant ways: For example, during preflight, (1) The animals preflight training was different (less well trained on the gaze task) and (2) the animals were exposed to more experimental manipulations (surgical and rotational). Inflight, (1) the animals were required to make a pointing gesture (motor response) in association with eye movements to obtain reward, (2) the inflight diet was different (more balanced), (3) the feeder for one of the animals clogged following 9 days of flight resulting in evident dehydration and probably less head motion exposure in that monkey and (4) there was limited video taping of the monkeys in space. During postflight, (1) we were unable to test the flight animals until 26 hours postflight as compared to 14.5 hours during Cosmos 2044, (2) the animals received significantly more exposure to motion stimuli during postflight testing than during Cosmos 2044. These differences in the vestibular environment will require analysis of several parameters other than just neural and eye movement responses. For example, computer programs will have to be written and used to recover and quantify the number of head movements made by each animal during flight. This activity is critical to the production of neural adaptation and increased gain.

Correia, Manning J.↗

NASA NTRS1997

Baroreflex regulation of blood pressure during dynamic exercise

From the work of Potts et al. Papelier et al. and Shi et al. it is readily apparent that the arterial (aortic and carotid) baroreflexes are reset to function at the prevailing ABP of exercise. The blood pressure of exercise is the result of the hemodynamic (cardiac output and TPR) responses, which appear to be regulated by two redundant neural control systems, "Central Command" and the "exercise pressor reflex". Central Command is a feed-forward neural control system that operates in parallel with the neural regulation of the locomotor system and appears to establish the hemodynamic response to exercise. Within the central nervous system it appears that the HLR may be the operational site for Central Command. Specific neural sites within the HLR have been demonstrated in animals to be active during exercise. With the advent of positron emission tomography (PET) and single-photon emission computed tomography (SPECT), the anatomical areas of the human brain related to Central Command are being mapped. It also appears that the Nucleus Tractus Solitarius and the ventrolateral medulla may serve as an integrating site as they receive neural information from the working muscles via the group III/IV muscle afferents as well as from higher brain centers. This anatomical site within the CNS is now the focus of many investigations in which arterial baroreflex function, Central Command and the "exercise pressor reflex" appear to demonstrate inhibitory or facilitatory interaction. The concept of whether Central Command is the prime mover in the resetting of the arterial baroreceptors to function at the exercising ABP or whether the resetting is an integration of the "exercise pressor reflex" information with that of Central Command is now under intense investigation. However, it would be justified to conclude, from the data of Bevegard and Shepherd, Dicarlo and Bishop, Potts et al., and Papelier et al. that the act of exercise results in the resetting of the arterial baroreflex. In addition, if, as we have proposed, the cardiopulmonary baroreceptors primarily monitors and reflexly regulates cardiac filling volume, it would seem from the data of Mack et al. and Potts et al. that the cardiopulmonary baroreceptor is also reset at the beginning of exercise. Therefore, investigations of the neural mechanisms of regulation involving Central Command and cardiopulmonary afferents, similar to those being undertaken for the arterial baroreflex, need to be established.

NASA Discipline Cardiopulmonary↗

NASA NTRS1990

Programmable Analog Memory Resistors For Electronic Neural Networks

Electrical resistance of new solid-state device altered repeatedly by suitable control signals, yet remains at steady value when control signal removed. Resistance set at low value ("on" state), high value ("off" state), or at any convenient intermediate value and left there until new value desired. Circuits of this type particularly useful in nonvolatile, associative electronic memories based on models of neural networks. Such programmable analog memory resistors ideally suited as synaptic interconnects in "self-learning" neural nets. Operation of device depends on electrochromic property of WO3, which when pure is insulator. Potential uses include nonvolatile, erasable, electronically programmable read-only memories.

Ramesham, Rajeshuni↗

NASA NTRS1992

More About Architecture For Intelligent Robotic Control

Boolean neural networks proposed to implement part of intermediate level of hierarchical architecture of control system for artificially intelligent control of robot hand. Concept described in "Architecture for Intelligent Control of Robotic Tasks" (NPO-17871). Rule level of architecture implemented in two Boolean neural networks operated and updated in alternation. No explicit programming of network. Internal configuration not unique but, depends on initial state and history of previous adaptations. Accepts new rules sequentially presented by external controller.

Fiorini, Paolo↗

NASA NTRS2009

Swarm autonomic agents with self-destruct capability

Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection.

Hinchey, Michael G.↗

NASA NTRS2011

Swarm autonomic agents with self-destruct capability

Hinchey, Michael G.↗

NASA NTRS1990

High-order neural models for error correcting code

The decoding and error-correction of data transmitted over a noisy channel is in principle equivalent to the operation of a neural network performing as a content-addressable memory. For a successful application, however, the neural network has to be capable of storing arbitrary words, and it has to be guaranteed that the stored words represent the only stable attractors of the memory. This paper presents a novel high-order neural network architecture that has these characteristics. The analog nature of the network can be used to perform soft-decision decoding with any block code. The performance in terms of postdecoding bit error rate versus SNR is demonstrated for two exemplary block codes. The comparison with a conventional decoding algorithm for a (15,5) cyclic redundancy code shows, for example, that the bit error rate at 7dB SNR can be decreased by two orders of magnitude.

Jeffries, Clark↗

NASA NTRS1997

Oil Reservoir Properties Estimation Using Neutal Networks

This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. Our approach relies on JPL's adjoint operators general purpose neural network code to determine the best suited architecture. We believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters.

oil reservoir neural networks geoscience data oil ↗

NASA NTRS2014

The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: Extent, Longevity, and Neural Bases

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility, and alterations in brain structure and function, in association with extended bed rest.

Seidler, R. D.↗

NASA NTRS2017

Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that are conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. We have collected data on several crewmembers and preliminary findings will be presented. Eventual comparison to results from our parallel bed rest study will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe.

Seidler, R. D.↗

NASA NTRS2001

Neural blockade during exercise augments central command's contribution to carotid baroreflex resetting

This investigation was designed to determine central command's role on carotid baroreflex (CBR) resetting during exercise. Nine volunteer subjects performed static and rhythmic handgrip exercise at 30 and 40% maximal voluntary contraction (MVC), respectively, before and after partial axillary neural blockade. Stimulus-response curves were developed using the neck pressure-neck suction technique and a rapid pulse train protocol (+40 to -80 Torr). Regional anesthesia resulted in a significant reduction in MVC. Heart rate (HR) and ratings of perceived exertion (RPE) were used as indexes of central command and were elevated during exercise at control force intensity after induced muscle weakness. The CBR function curves were reset vertically with a minimal lateral shift during control exercise and exhibited a further parallel resetting during exercise with neural blockade. The operating point was progressively reset to coincide with the centering point of the CBR curve. These data suggest that central command was a primary mechanism in the resetting of the CBR during exercise. However, it appeared that central command modulated the carotid-cardiac reflex proportionately more than the carotid-vasomotor reflex.

NASA Discipline Cardiopulmonary↗

NASA NTRS2014

The Effects of Long Duration Head Down Tilt Bed Rest on Neurocognitive Performance: The Effects of Exercise Interventions

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. Our ongoing bed rest participants are also engaging in exercise studies directed by Dr. Lori Ploutz Snyder. In this presentation, I will briefly highlight the existing literature linking exercise and fitness to brain and behavioral functions. I will also overview the metrics from my study that could be investigated in relation to the exercise and control subgroups.

Seidler, R. D.↗

NASA NTRS1992

Neural joint control for Space Shuttle Remote Manipulator System

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.

Atkins, Mark A.↗

NASA NTRS1991

Adjoint-operators and non-adiabatic learning algorithms in neural networks

Adjoint sensitivity equations are presented, which can be solved simultaneously (i.e., forward in time) with the dynamics of a nonlinear neural network. These equations provide the foundations for a new methodology which enables the implementation of temporal learning algorithms in a highly efficient manner.

Toomarian, N.↗

NASA NTRS1992

Application of space-time neural networks to detect tether skiprope phenomenon in space operations

The feasibility of operating tethered payloads in earth orbit will be studied during a space shuttle flight scheduled for 1992. Tethered systems may exhibit a circular transverse oscillation or skiprope phenomenon due to interaction between the earth's magnetic field and current pulsing through the tether. Effective damping of this skiprope motion depends on rapid and accurate detection of its magnitude and phase. Satellite attitude motion has characteristic oscillations as well as many other perturbations and therefore the relationship between skiprope parameters and attitude time history is very complex and nonlinear. A space-time neural network (STNN) for filtering satellite rate gyro data is proposed for rapid detection and prediction of skiprope magnitude and phase. A validated orbital operations simulator and STNN software will be used for training and testing of this skiprope detection system. The advantages of STNNs are discussed and STNN configurations and preliminary results are presented.

Lea, Robert N.↗