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At least 343 records · Page 19

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NASA NTRS1989

Monolithically integrated two-dimensional arrays of optoelectronic threshold devices for neural network applications

A monolithic 10 x 10 two-dimensional array of 'optical neuron' optoelectronic threshold elements for neural network applications has been designed, fabricated, and tested. Overall array dimensions are 5 x 5 mm, while the individual neurons, composed of an LED that is driven by a double-heterojunction bipolar transistor, are 250 x 250 microns. The overall integrated structure exhibited semiconductor-controlled rectifier characteristics, with a breakover voltage of 75 V and a reverse-breakdown voltage of 60 V; this is attributable to the parasitic p-n-p transistor which exists as a result of the sharing of the same n-AlGaAs collector between the transistors and the LED.

Kim, J. H.↗

NASA NTRS1989

Limitations of opto-electronic neural networks

Consideration is given to the limitations of implementing neurons, weights, and connections in neural networks for electronics and optics. It is shown that the advantages of each technology are utilized when electronically fabricated neurons are included and a combination of optics and electronics are employed for the weights and connections. The relationship between the types of neural networks being constructed and the choice of technologies to implement the weights and connections is examined.

Yu, Jeffrey↗

NASA NTRS1989

Receptive fields and visual representations

Efficient representation of images for human use requires an understanding of how the brain processes and represents visual information. Spatial imagery is represented in the brain in the receptive fields of visual neurons. Models of these neurons lead to models of image fidelity, and to digital implementations of these neural codes. This approach will be illustrated by two example codes. The advantages and difficulties of this approach will be discussed.

Watson, Andrew B.↗

NASA NTRS1990

A discrepancy within primate spatial vision and its bearing on the definition of edge detection processes in machine vision

The visual perception of form information is considered to be based on the functioning of simple and complex neurons in the primate striate cortex. However, a review of the physiological data on these brain cells cannot be harmonized with either the perceptual spatial frequency performance of primates or the performance which is necessary for form perception in humans. This discrepancy together with recent interest in cortical-like and perceptual-like processing in image coding and machine vision prompted a series of image processing experiments intended to provide some definition of the selection of image operators. The experiments were aimed at determining operators which could be used to detect edges in a computational manner consistent with the visual perception of structure in images. Fundamental issues were the selection of size (peak spatial frequency) and circular versus oriented operators (or some combination). In a previous study, circular difference-of-Gaussian (DOG) operators, with peak spatial frequency responses at about 11 and 33 cyc/deg were found to capture the primary structural information in images. Here larger scale circular DOG operators were explored and led to severe loss of image structure and introduced spatial dislocations (due to blur) in structure which is not consistent with visual perception. Orientation sensitive operators (akin to one class of simple cortical neurons) introduced ambiguities of edge extent regardless of the scale of the operator. For machine vision schemes which are functionally similar to natural vision form perception, two circularly symmetric very high spatial frequency channels appear to be necessary and sufficient for a wide range of natural images. Such a machine vision scheme is most similar to the physiological performance of the primate lateral geniculate nucleus rather than the striate cortex.

Jobson, Daniel J.↗

NASA NTRS1991

Biological neural networks as model systems for designing future parallel processing computers

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

Ross, Muriel D.↗

NASA NTRS1992

Method Accelerates Training Of Some Neural Networks

Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

Shelton, Robert O.↗

NASA NTRS1993

Generalized Adaptive Artificial Neural Networks

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Tawel, Raoul↗

NASA NTRS1989

Dopamine release in rat striatum - Physiological coupling to tyrosine supply

Intracerebral microdialysis was used to monitor dopamine release in rat striatal extracellular fluid following the intraperitoneal administration of dopamine's precursor amino acid, L-tyrosine. Dopamine concentrations in dialysates increased transiently after tyrosine (50-100 mg/kg) administration. Pretreatment with haloperidol or the partial lesioning of nigrostriatal neurons enhanced the effect of tyrosine on dopamine release, and haloperidol also prolonged this effect. These data suggest that nigrostriatal dopaminergic neurons are responsive to changes in precursor availability under basal conditions, but that receptor-mediated feedback mechanisms limit the magnitude and duration of this effect.

During, Matthew J.↗

NASA NTRS1988

Effects of dietary amino acids, carbohydrates, and choline on neurotransmitter synthesis

The ability of a meal to increase or decrease brain neurotransmitter synthesis has been studied. It is concluded that brain serotonin synthesis is directly controlled by the proportions of carbohydrate to protein in meals and snacks that increase or decrease brain tryptophan levels, thereby changing the substrate saturation of tryptophan hydroxylase and the rate of serotonin synthesis. The ability of serotoninergic neurons to have their output coupled to dietary macronutrients enables them to function as sensors of peripheral metabolism, and to subserve an important role in the control of appetite. The robust and selective responses of catecholaminergic and cholinergic neurons to supplemental tyrosine and choline suggest that these compounds may become useful as a new type of drug for treating deseases or conditions in which adequate quantities of the transmitter would otherwise be unavailable.

Wurtman, Richard J.↗

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 NTRS1991

Vestibular afferent responses to microrotational stimuli

Intracellular microelectrode recording/labeling techniques were used to investigate vestibular afferent responses in the bullfrog, to very small amplitude (less than 5 deg p-p) sinusoidal rotations in the vertical plane over the frequency range of 0.063-4 Hz. Robust responses to peak accelerations as low as 0.031 deg/sec per sec were obtained from units subsequently traced to either the central portion of the anterior canal crista or the striolar region of the utricle. All of these microrotationally sensitive afferent neurons had irregular resting discharge rates, and the majority had transfer ratios (relative to rotational velocity) of 1-40 spikes/sec per deg/sec. Individual utricular afferent velocity transfer ratios were nearly constant over the frequency range of 0.125-4 Hz. Canal units displayed decreasing response transfer ratios as stimulus frequencies increased. These findings indicate that, although utricular striolar and central crista afferent velocity transfer ratios to microrotations were very similar, utricular striolar afferent neurons were more faithful sensors of very small amplitude rotational velocity in the vertical plane.

Myers, Steven F.↗

NASA NTRS1986

Foods and food constituents that affect the brain and human behavior

Until recently, it was generally believed that brain function was usually independent of day-to-day metabolic changes associated with consumption of food. Although it was acknowledged that peripheral metabolic changes associated with hunger or satiety might affect brain function, other effects of foods on the brain were considered unlikely. However, in 1971, Fernstrom and Wurtman discovered that under certain conditions, the protein-to-carbohydrate ratio of a meal could affect the concentration of a particular brain neurotransmitter. That neurotransmitter, serotonin, participates in the regulation of a variety of central nervous system (CNS) functions including sleep, pain sensitivity, aggression, and patterns of nutrient selection. The activity of other neurotransmitter systems has also been shown to be, under certain conditions, affected by dietary constituents which are given either as ordinary foods or in purified form. For example, the CNS turnover of two catecholamine neurotransmitters, dopamine and norepinephrine, can be altered by ingestion of their amino acid precursor, tyrosine, when neurons that release these monoamines are firing frequently. Similarly, lecithin, a dietary source of choline, and choline itself have been shown to increase the synthesis of acetylcholine when cholinergic neurons are very active. It is possible that other neurotransmitters could also be affected by precursor availability or other, as yet undiscovered peripheral factors governed by food consumption. The effects of food on neurotransmitters and behavior are discussed.

Lieberman, Harris R.↗

NASA NTRS1990

Auto-programmable impulse neural circuits

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Watula, D.↗

NASA NTRS1993

Unipolar terminal-attractor based neural associative memory with adaptive threshold

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Liu, Hua-Kuang↗

NASA NTRS1992

A mean field neural network for hierarchical module placement

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Unaltuna, M. Kemal↗

NASA NTRS1995

Neurodynamic oscillators

Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

Espinosa, Ismael↗

NASA NTRS1994

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Sheu, Bing J.↗

NASA NTRS1995

Chemical Topography of Efferent Projections from the Median Preoptic Nucleus to Pontine Monoaminergic Cell Groups in the Rat

This study examined efferent output from the median preoptic nucleus (MNPO) to pontine noradrenergic and serotonergic cell groups using an anterograde tracing technique (Phaseolus vulgaris leucoagglutinin, PHA-L) combined with glucose oxidase immunocytochemistry to serotonin (5-HT) or to dopamine-beta-hydroxylase (DBH). Injections of PHA-L into the ventral MNPO resulted in moderate axonal labeling within the region of the B7 and B8 serotonergic groups in the dorsal raphe. PHA-L labeled fibers and punctate processes were observed in close apposition to many of the 5-HT immunoreactive neurons in these regions. In contrast, sparse terminal labeling was found within the B5 group in the raphe pontis nucleus, and only trace fiber labeling observed in the B3 and B6 groups. Efferents from the MNPO also provided moderate innervation to the A6 and A7 noradrenergic groups. PHA-L labeled punctate processes were found most frequently in close apposition to DBH-immunoreactive neurons at mid- to caudal levels of the locus coeruleus. Some labeled axons were also present within the A7 and A5 groups. Additionally, a close apposition between labeled MNPO efferents and 5-HT fibers within the lateral parabrachial nucleus was observed. The results indicate the MNPO provides a topographic innervation of monoaminergic groups in the upper brainstem.

Zardetto-Smith, Andrea M.↗