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NASA NTRS2000

Postural control model interpretation of stabilogram diffusion analysis

Collins and De Luca [Collins JJ. De Luca CJ (1993) Exp Brain Res 95: 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation (error signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs. This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.

NASA Program Biomedical Research and Countermeasur↗

NASA NTRS1987

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc.

Greenwood, Dan↗

NASA NTRS2014

Carbon Nanofiber Nanoelectrode Arrays for Neural Implant Devices

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable smart therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

carbon nanotubes↗

NASA NTRSDate not supplied

High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗

NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Materials discovery↗

NASA NTRS2001

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Niewoehner, Kevin R.↗

NASA NTRS2003

Physiological and Molecular Genetic Effects of Time-Varying Electromagnetic Fields on Human Neuronal Cells

The present investigation details the development of model systems for growing two- and three-dimensional human neural progenitor cells within a culture medium facilitated by a time-varying electromagnetic field (TVEMF). The cells and culture medium are contained within a two- or three-dimensional culture vessel, and the electromagnetic field is emitted from an electrode or coil. These studies further provide methods to promote neural tissue regeneration by means of culturing the neural cells in either configuration. Grown in two dimensions, neuronal cells extended longitudinally, forming tissue strands extending axially along and within electrodes comprising electrically conductive channels or guides through which a time-varying electrical current was conducted. In the three-dimensional aspect, exposure to TVEMF resulted in the development of three-dimensional aggregates, which emulated organized neural tissues. In both experimental configurations, the proliferation rate of the TVEMF cells was 2.5 to 4.0 times the rate of the non-waveform cells. Each of the experimental embodiments resulted in similar molecular genetic changes regarding the growth potential of the tissues as measured by gene chip analyses, which measured more than 10,000 human genes simultaneously.

Goodwin, Thomas J.↗

NASA NTRS1999

Control of skeletal muscle perfusion at the onset of dynamic exercise

At the onset of exercise there is a rapid increase in skeletal muscle vascular conductance and blood flow. Several mechanisms involved in the regulation of muscle perfusion have been proposed to initiate this hyperemic response, including neural, metabolic, endothelial, myogenic, and muscle pump mechanisms. Investigators utilizing pharmacological blockade of cholinergic muscarinic receptors and sympathectomy have concluded that neither sympathetic cholinergic nor adrenergic neural mechanisms are involved in the initial hyperemia. Studies have also shown that the time course for vasoactive metabolite release, diffusion, accumulation, and action is too long to account for the rapid increase in vascular conductance at the initiation of exercise. Furthermore, there is little or no evidence to support an endothelium or myogenic mechanism as the initiating factor in the muscle hyperemia. Thus, the rise in muscle blood flow does not appear to be explained by known neural, metabolic, endothelial, or myogenic influences. However, the initial hyperemia is consistent with the mechanical effects of the muscle pump to increase the arteriovenous pressure gradient across muscle. Because skeletal muscle blood flow is regulated by multiple and redundant mechanisms, it is likely that neural, metabolic, and possibly endothelial factors become important modulators of mechanically induced exercise hyperemia following the first 5-10 s of exercise.

Review, Tutorial↗

NASA NTRS2020

Genetic Algorithm for Optimization of Neural Networks for Bayesian Inference of Model Uncertainty

The objective of this work was to develop a genetic optimization algorithm that can design a neural network capable of producing uncertainty estimates along with predictions. This algorithm is necessary because the inclusion of uncertainty modeling in a neural network greatly complicates the network’s design space, making the development of a converging model extremely difficult and time consuming. The genetic algorithm presented in this work uses a number of value ranges for various configurable neural network parameters to create a randomly generated population of network architectures. The initially generated population is then evolved over the course of several generations, with the best performing models breeding to produce novel network configurations. Mutations are randomly applied to the network designs to facilitate the development of adaptations beneficial to the task being performed. An experiment was conducted to validate the proposed algorithm, in which the genetic optimizer was tasked with producing a neural network capable of predicting the sound pressure level (SPL) resulting from jet-surface interaction (JSI) noise. The data used for this task was generated at the NASA Glenn Research Center in the Aero-Acoustic Propulsion Laboratory. Starting with an initial population size of 35 randomly generated networks, and evolved over the course of 10 generations, the genetic algorithm produced a design able to predict SPL as a result of JSI noise within 0.272 dB, on average.

Genetic algorithm↗

NASA NTRS2021

Predicting near-saturated hydraulic conductivity in urban soils

Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (K(n)). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban K(n), 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban K(n), with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between K(n) and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.

Jinshi Jian↗

NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of Smart Deep Brain Stimulation

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable "smart" therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

bioinstrumentation↗

NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of "Smart" Deep Brain Stimulation

human body↗

NASA NTRS1990

Nonvolatile Ionic Two-Terminal Memory Device

Conceptual solid-state memory device nonvolatile and erasable and has only two terminals. Proposed device based on two effects: thermal phase transition and reversible intercalation of ions. Transfer of sodium ions between source of ions and electrical switching element increases or decreases electrical conductance of element, turning switch "on" or "off". Used in digital computers and neural-network computers. In neural networks, many small, densely packed switches function as erasable, nonvolatile synaptic elements.

Williams, Roger M.↗

NASA NTRS2017

Behavioral Assessment of Spaceflight Effects on Neurocognitive Performance: Extent and Longevity

Exposure to the microgravity environment during spaceflight missions impacts crewmembers' sensorimotor function. Bock et al. [1] studied the cognitive demands of human sensorimotor performance and dual tasking during long duration missions and concluded that both stress and scarcity of cognitive resources required for sensorimotor adaptation may be responsible for these deficits during spaceflight. Therefore, in consideration of the health and performance of crewmembers in- and post-flight, we are conducting this study to investigate the effects of spaceflight on the extent, longevity and neural bases of sensorimotor, cognitive, and neural changes. The data presented will focus on the behavioral measures that were collected pre-, in- and post-flight including spatial cognition, processing speed, bimanual coordination, functional mobility, computerized dynamic posturography (CDP), and vibrotactile induced vestibular evoked myogenic potential (VEMP). To date, data were collected over the course of two pre-flight sessions and four post-flight sessions on five crewmembers (n=13) using the protocol described in Koppelmans et al. [2]. Balance control was assessed using CDP, with eyes closed and a sway-referenced base of support (Sensory Organization Test 5), with and without head movements in the pitch plane. Spatial working memory was assessed using Thurston's Card Rotation Test and a Mental Rotation Test. The Rod and Frame Test was performed to test visual dependence. The Digit Symbol Substitution Test was performed to evaluate processing speed, and the Purdue Pegboard Task was performed to test bimanual coordination. Vestibular function was assessed by eliciting ocular VEMP via a hand held striker on the side of the head as subjects lay supine on a gurney. Subjects also performed the Functional Mobility Test of walking through an obstacle course to assess rate of early motor learning. Data were also collected on the same crewmembers during three in-flight sessions on the International Space Station (ISS). In-flight, spatial working memory was assessed using the Mental Rotation Test, adaptation to visuo-motor transformation in manual control was assessed using the Sensorimotor Adaptation Test, and multi-tasking ability was assessed using the Dual Task Test. These three tests were performed in a strapped-in configuration mimicking a seated position - waist bungees pulled the crewmember toward the "floor" with feet secured in foot loops. The Mental Rotation Test was also performed in a free-floating configuration while the crewmember floated while holding on to the gamepad controller used to provide input that was secured to the equipment rack on the ISS. Preliminary findings from data collected to date, will be included in the presentation. Eventual comparison to results from supporting bed rest and longitudinal studies will enable the parsing out of the multiple mechanisms contributing to any observed spaceflight-induced sensorimotor and cognitive behavioral changes.

De Dios, Y. E.↗

NASA NTRS2017

Behavioral Assessment of Spaceflight Effects on Neurocognitive Performance - Extent and Longevity

Exposure to the microgravity environment during a spaceflight mission impacts crewmembers' sensorimotor function. A study conducted by Bock et al. concluded that stress and scarcity of cognitive resources required for sensorimotor adaptation may be responsible for deficits during spaceflight. We are conducting this study to investigate the effects of spaceflight on the extent, longevity and neural bases of sensorimotor, cognitive, and neural changes. The data presented will focus on the behavioral measures that were collected pre-, in- and post -flight.

De Dios, Y.E.↗

NASA NTRS1989

Manganese oxide microswitch for electronic memory based on neural networks

A solid-state, resistance tailorable, programmable-once, binary, nonvolatile memory switch based on manganese oxide thin films is reported. MnO(x) exhibits irreversible memory switching from conducting (on) to insulating (off) state, with the off and on resistance ratio of greater than 10,000. The switching mechanism is current-triggered chemical transformation of a conductive MnO(2-Delta) to an insulating Mn2O3 state. The energy required for switching is of the order of 4-20 nJ/sq micron. The low switching energy, stability of the on and off states, and tailorability of the on state resistance make these microswitches well suited as programmable binary synapses in electronic associative memories based on neural network models.

Ramesham, R.↗

NASA NTRS2013

Conductance of Ion Channels - Theory vs. Experiment

Transmembrane ion channels mediate a number of essential physiological processes in a cell ranging from regulating osmotic pressure to transmission of neural signals. Kinetics and selectivity of ion transport is of critical importance to a cell and, not surprisingly, it is a subject of numerous experimental and theoretical studies. In this presentation we will analyze in detail computer simulations of two simple channels from fungi - antiamoebin and trichotoxin. Each of these channels is made of an alpha-helical bundle of small, nongenomically synthesized peptides containing a number of rare amino acids and exhibits strong antimicrobial activity. We will focus on calculating ionic conductance defined as the ratio of ionic current through the channel to applied voltage. From molecular dynamics simulations, conductance can be calculated in at least two ways, each involving different approximations. Specifically, the current, given as the number of charges transferred through the channel per unit of time, can be obtained from the number of events in which ions cross the channel during the simulation. This method works well for large currents (high conductance values and/or applied voltages). If the number of crossing events is small, reliable estimates of current are difficult to achieve. Alternatively, conductance can be estimated assuming that ion transport can be well approximated as diffusion in the external potential given by the free energy profile. Then, the current can be calculated by solving the one-dimensional diffusion equation in this external potential and applied voltage (the generalized Nernst-Planck equation). To do so three ingredients are needed: the free energy profile, the position-dependent diffusion coefficient and the diffusive flux of ions into the channel. All these quantities can be obtained from molecular dynamics simulations. An important advantage of this method is that it can be used equally well to estimating large and small currents. In addition, once the free energy profile becomes available the full current-voltage dependence can be readily obtained. For both channels we carried out calculations using both approaches. We also tested the main assumptions underlying the diffusive model, such as uncorrelated nature of individual crossing events and Fickian diffusion. The accuracy and consistency of different methods will be discussed. Finally we will discuss how comparisons between calculated and measured ionic conductance and selectivity of transport can be used for determining structural models of the channels.

Pohorille, Andrew↗

NASA NTRS2003

Persistent neural activity in head direction cells

Many neurons throughout the rat limbic system discharge in relation to the animal's directional heading with respect to its environment. These so-called head direction (HD) cells exhibit characteristics of persistent neural activity. This article summarizes where HD cells are found, their major properties, and some of the important experiments that have been conducted to elucidate how this signal is generated. The number of HD and angular head velocity cells was estimated for several brain areas involved in the generation of the HD signal, including the postsubiculum, anterior dorsal thalamus, lateral mammillary nuclei and dorsal tegmental nucleus. The HD cell signal has many features in common with what is known about how neural integration is accomplished in the oculomotor system. The nature of the HD cell signal makes it an attractive candidate for using neural network models to elucidate the signal's underlying mechanisms. The conditions that any network model must satisfy in order to accurately represent how the nervous system generates this signal are highlighted and areas where key information is missing are discussed.

Non-NASA Center↗