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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Muscone suppresses inflammatory responses and neuronal damage in a rat model of cervical spondylotic myelopathy by regulating Drp1‐dependent mitochondrial fission

Abstract Cervical spondylotic myelopathy (CSM) is a common cause of disability with few treatments. Aberrant mitochondrial dynamics play a crucial role in the pathogenesis of various neurodegenerative diseases. Thus, regulation of mitochondrial dynamics may offer therapeutic benefit for the treatment of CSM. Muscone, the active ingredient of an odoriferous animal product, exhibits anti‐inflammatory and neuroprotective effects for which the underlying mechanisms remain obscure. We hypothesized that muscone might ameliorate inflammatory responses and neuronal damage by regulating mitochondrial dynamics. To this end, the effects of muscone on a rat model of chronic cervical cord compression, as well as activated BV2 cells and injured neurons, were assessed. The results showed that muscone intervention improved motor function compared with vehicle‐treated rats. Indeed, muscone attenuated pro‐inflammatory cytokine expression, neuronal‐apoptosis indicators in the lesion area, and activation of the nod‐like receptor family pyrin domain‐containing 3 inflammasome, nuclear transcription factor‐κB, and dynamin‐related protein 1 in Iba1‐ and βIII‐tubulin‐labeled cells. Compared with vehicle‐treated rats, compression sites of muscone‐treated animals exhibited elongated mitochondrial morphologies in individual cell types and reduced reactive oxygen species. In vitro results indicated that muscone suppressed microglial activation and neuronal damage by regulating related‐inflammatory or apoptotic molecules. Moreover, muscone inhibited dynamin‐related protein 1 activation in activated BV2 cells and injured neurons, whereby it rescued mitochondrial fragmentation and reactive oxygen species production, which regulate a wide range of inflammatory and apoptotic molecules. Our findings reveal that muscone attenuates neuroinflammation and neuronal damage in rats with chronic cervical cord compression by regulating mitochondrial fission events, suggesting its promise for CSM therapy. image

Zhou, Long‐yun↗
DOE OSTI2020

FOXO3 targets are reprogrammed as Huntington's disease neural cells and striatal neurons face senescence with p16 INK4a increase

Neurodegenerative diseases (ND) have been linked to the critical process in aging-cellular senescence. However, the temporal dynamics of cellular senescence in ND conditions is unresolved. Here, we show senescence features develop in human Huntington's disease (HD) neural stem cells (NSCs) and medium spiny neurons (MSNs), including the increase of p16 INK4a , a key inducer of cellular senescence. We found that HD NSCs reprogram the transcriptional targets of FOXO3, a major cell survival factor able to repress cell senescence, antagonizing p16 INK4a expression via the FOXO3 repression of the transcriptional modulator ETS2. Additionally, p16 INK4a promotes cellular senescence features in human HD NSCs and MSNs. These findings suggest that cellular senescence may develop during neuronal differentiation in HD and that the FOXO3-ETS2-p16 INK4a axis may be part of molecular responses aimed at mitigating this phenomenon. Our studies identify neuronal differentiation with accelerated aging of neural progenitors and neurons as an alteration that could be linked to NDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer’s disease

The molecular mechanisms underlying neuronal dysfunction in Alzheimer’s disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant’s real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau’s synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Vinculin is required for neuronal mechanosensing but not for axon outgrowth

Integrin receptors are transmembrane proteins that bind to the extracellular matrix (ECM). In most animal cell types integrins cluster together with adaptor proteins at focal adhesions that sense and respond to external mechanical signals. In the central nervous system (CNS), ECM proteins are sparsely distributed, the tissue is comparatively soft and neurons do not form focal adhesions. Thus, how neurons sense tissue stiffness is currently poorly understood. Here, we found that integrins and the integrin-associated proteins talin and focal adhesion kinase (FAK) are required for the outgrowth of neuronal processes. Vinculin, however, whilst not required for neurite outgrowth was a key regulator of integrin-mediated mechanosensing of neurons. During growth, growth cones of axons of CNS derived cells exerted dynamic stresses of around 10–12 Pa on their environment, and axons grew significantly longer on soft (0.4 kPa) compared to stiff (8 kPa) substrates. Depletion of vinculin blocked this ability of growth cones to distinguish between soft and stiff substrates. These data suggest that vinculin in neurons acts as a key mechanosensor, involved in the regulation of growth cone motility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Intrinsic and Extrinsic Factors Influencing the Dynamics of VO 2 Mott Oscillators

Oscillatory devices have gained significant interest recently as key components of computing systems based on biomimetic neuronal spiking. An understanding of the time scales underlying the spiking is essential for engineering fast, controllable, low energy devices. However, we find that the intrinsic dynamics of these devices are difficult to properly characterize, as they can be heavily influenced by the external circuitry used to measure them. Here we demonstrate these challenges using a VO 2 Mott oscillator with a sub-100 nm effective size, achieved using a nanogap cut in a metallic carbon nanotube electrode. Given the nanoscale thermal volume of this device, it would be expected to exhibit rapid oscillations. However, due to external parasitics present within commonly used current sources, we see orders of magnitude slower dynamics. Here, we outline methods for determining when measurements are dominated by extrinsic factors and discuss the operating conditions under which intrinsic oscillation frequencies may be observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Artificial extracellular matrix scaffolds of mobile molecules enhance maturation of human stem cell-derived neurons

Human induced pluripotent stem cell (hiPSC) technologies offer a unique resource for modeling neurological diseases. However, iPSC models are fraught with technical limitations including abnormal aggregation and inefficient maturation of differentiated neurons. These problems are in part due to the absence of synergistic cues of the native extracellular matrix (ECM). We report on the use of three artificial ECMs based on peptide amphiphile (PA) supramolecular nanofibers. All nanofibers display the laminin-derived IKVAV signal on their surface but differ in the nature of their non-bioactive domains. Here, we find that nanofibers with greater intensity of internal supramolecular motion have enhanced bioactivity toward hiPSC-derived motor and cortical neurons. Proteomic, biochemical, and functional assays reveal that highly mobile PA scaffolds caused enhanced β1-integrin pathway activation, reduced aggregation, increased arborization, and matured electrophysiological activity of neurons. Our work highlights the importance of designing biomimetic ECMs to study the development, function, and dysfunction of human neurons.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

42 ENGINEERING↗
DOE OSTI2023

Phase-field model of coupled insulator-metal transitions and oxygen vacancy redox reactions

A predominant ubiquitous feature of strongly correlated oxides is the possible presence of oxygen vacancies, which has recently been shown to have profound effects on their electronic phase transitions. Here, we formulate a comprehensive phase-field model of intercoupled insulator-metal transitions and oxygen vacancy redox reactions, taking into account the valence electron state of oxygen vacancies. We use the model to study the voltage self-oscillation phenomenon in a prototypical strongly correlated oxide, VO 2 , and discover the mutual activation of the insulator-metal transition and oxygen vacancy redox reactions leading to systematic enhancement of the oscillation frequency. Furthermore, the established methodology and the mutual activation mechanism are generally applicable to understanding any insulator-metal transition dynamics in oxygen-deficient correlated oxides and improving the performance of voltage self-oscillation-based artificial neurons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2024

Network-level encoding of local neurotransmitters in cortical astrocytes

Abstract Astrocytes, the most abundant non-neuronal cell type in the mammalian brain, are crucial circuit components that respond to and modulate neuronal activity through calcium (Ca 2+ ) signalling 1–7 . Astrocyte Ca 2+ activity is highly heterogeneous and occurs across multiple spatiotemporal scales—from fast, subcellular activity 3,4 to slow, synchronized activity across connected astrocyte networks 8–10 —to influence many processes 5,7,11 . However, the inputs that drive astrocyte network dynamics remain unclear. Here we used ex vivo and in vivo two-photon astrocyte imaging while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca 2+ responses beyond an individual stimulated cell. Further, we find that a key subset of Ca 2+ activity—propagative activity—differentiates astrocyte network responses to these two main neurotransmitters, and may influence responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over a minutes-long time course, contributing to accumulating evidence that substantial astrocyte–neuron communication occurs across slow, network-level spatiotemporal scales 12–14 . These findings will enable future studies to investigate the link between specific astrocyte Ca 2+ activity and specific functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.

Science & Technology - Other Topics↗
DOE OSTI2026

Protonic nickelate device networks for spatiotemporal neuromorphic computing

Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. Here we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO 3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO 3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanosecond-scale operation with an energy cost of ~0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

Electrical and electronic engineering↗
DOE OSTI2024

Electromechanical memcapacitor model offering biologically plausible spiking.

In this article, we introduce a new nanoscale electromechanical device - a leaky memcapacitor - and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movableplate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.

Zhang, Zixi↗
DOE OSTI2021

Ultrafast Neuromorphic Dynamics Using Hidden Phases in the Prototype of Relaxor Ferroelectrics

Materials possessing multiple states are promising to emulate synaptic and neuronic behaviors. Their operation frequency, typically in or below the GHz range, however, limits the speed of neuromorphic computing. Ultrafast THz electric field excitation has been employed to induce nonequilibrium states of matter, called hidden phases in oxides. One may wonder if there are systems for which THz pulses can generate neuronic and synaptic behavior, via the creation of hidden phases. Using atomistic simulations, we discover that relaxor ferroelectrics can emulate all the key neuronic and memristive synaptic features. Their occurrence originates from the activation of many hidden phases of polarization order, resulting from the response of nanoregions to THz pulses. We find such phases further possess different dielectric constants, which is also promising for memcapacitor devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

CTRL-STEER: Closed-Loop Neuron Activation Control in Vision-Language-Action Models

Vision-Language-Action (VLA) models enable test-time behavioral steering via neuron-level interventions, but existing methods use fixed strengths and operate in open loop. This static modulation fails under evolving task dynamics, leading to overcorrection, oscillations, and reduced task success—especially for temporal attributes like speed. We propose CTRL-STEER, a control-theoretic framework that casts activation steering as closed-loop feedback with adaptive, time-varying interventions. Instead of assuming neurons encode temporal concepts, we steer along motion-aligned residual directions and regulate intervention magnitude via feedback. We instantiate this with both PID and reinforcement learning controllers that jointly optimize concept adherence and task success. Experiments on fine-tuned OpenVLA policies across four LIBERO suites show improved stability and a better steering–success trade-off over fixed-coefficient baselines, without retraining the base model.

Babu, Abhijith [Florida International University, ↗