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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS1987

Electronic implementation of associative memory based on neural network models

An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.

Moopenn, A.↗
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↗
NASA NTRS1999

Functional organization of primate translational vestibulo-ocular reflexes and effects of unilateral labyrinthectomy

Translational vestibulo-ocular reflexes (trVORs) are characterized by distinct spatio-temporal properties and sensitivities that are proportional to the inverse of viewing distance. Anodal (inhibitory) labyrinthine stimulation (100 microA, < 2 s) during motion decreased the high-pass filtered dynamics, as well as horizontal trVOR sensitivity and its dependence on viewing distance. Cathodal (excitatory) currents had opposite effects. Translational VORs were also affected after unilateral labyrinthectomy. Animals lost their ability to modulate trVOR sensitivity as a function of viewing distance acutely after the lesion. These deficits partially recovered over time, albeit a significant reduction in trVOR sensitivity as a function of viewing distance remained in compensated animals. During fore-aft motion, the effects of unilateral labyrinthectomy were more dramatic. Both acute and compensated animals permanently lost their ability to modulate fore-aft trVOR responses as a function of target eccentricity. These results suggest that (1) the dynamics and viewing distance-dependent properties of the trVORs are very sensitive to changes in the resting firing rate of vestibular afferents and, consequently, vestibular nuclei neurons; (2) the most irregularly firing primary otolith afferents that are most sensitive to labyrinthine electrical stimulation might contribute to reflex dynamics and sensitivity; (3) inputs from both labyrinths are necessary for the generation of the translational VORs.

Non-NASA Center↗
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↗
NASA NTRS2002

Population dynamics during cell proliferation and neuronogenesis in the developing murine neocortex

During the development of the neocortex, cell proliferation occurs in two specialized zones adjacent to the lateral ventricle. One of these zones, the ventricular zone, produces most of the neurons of the neocortex. The proliferating population that resides in the ventricular zone is a pseudostratified ventricular epithelium (PVE) that looks uniform in routine histological preparations, but is, in fact, an active and dynamically changing population. In the mouse, over the course of a 6-day period, the PVE produces approximately 95% of the neurons of the adult neocortex. During this time, the cell cycle of the PVE population lengthens from about 8 h to over 18 h and the progenitor population passes through a total of 11 cell cycles. This 6-day, 11-cell cycle period comprises the "neuronogenetic interval" (NI). At each passage through the cell cycle, the proportion of daughter cells that exit the cell cycle (Q cells) increases from 0 at the onset of the NI to 1 at the end of the NI. The proportion of daughter cells that re-enter the cell cycle (P cells) changes in a complementary fashion from 1 at the onset of the NI to 0 at the end of the NI. This set of systematic changes in the cell cycle and the output from the proliferative population of the PVE allows a quantitative and mathematical treatment of the expansion of the PVE and the growth of the cortical plate that nicely accounts for the observed expansion and growth of the developing neocortex. In addition, we show that the cells produced during a 2-h window of development during specific cell cycles reside in a specific set of laminae in the adult cortex, but that the distributions of the output from consecutive cell cycles overlap. These dynamic events occur in all areas of the PVE underlying the neocortex, but there is a gradient of maturation that begins in the rostrolateral neocortex near the striatotelencephalic junction and which spreads across the surface of the neocortex over a period of 24-36 h. The presence of the gradient across the hemisphere is a possible source of positional information that could be exploited during development to establish the areal borders that characterize the adult neocortex.

Non-NASA Center↗
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↗
NASA NTRS2003

Physiological Maturation of Regenerating Hair Cells

The bullfrog saccule, a sensor of gravity and substrate-borne vibration, is a model system for hair cell transduction. Saccular hair cells also increase in number throughout adult life and rapidly recover after hair cell damage, making this organ an ideal system for studying hair cell development, repair, and regeneration. We have used of hair cell and supporting cell immunocytochemical markers to identify damaged hair cells and hair cell precursors in organotypic cultures of the bullfrog saccule. We then used an innovative combination of confocal, electron, and time-lapse microscopy to study the fate of damaged hair cells and the origin of new hair cells after gentamicin ototoxicity in normal and mitotically blocked saccular cultures. These studies have shown that gentamicin ototoxicity produces both lethal and sublethal hair cell damage. They have also shown that hair cell recovery in this organ takes place by both the repair of sublethally damaged hair cells and by the replacement of lost hair cells by mitotic regeneration. In parallel studies, we have used biophysical and molecular biological techniques to study the differentiation and innervation of developing, repairing, and regenerating hair cells. More specifically, we have used RT-PCR to obtain the bullfrog homologues of L-type voltage- gated calcium (L-VGCC) and large-conductance Ca(2+)-activated potassium (BK) channel genes. We have then obtained probes for these genes and, using in situ hybridization, begun to examine their expression in the bullfrog saccule and amphibian papilla. We have also used fluorescent-labeled channel toxins and channel toxin derivatives to determine the time of appearance of L-type voltage-gated calcium (L-VGCC) and Ca(2+)-activated potassium (BK) channels and to study dynamic changes in the number, distribution, and co-localization of these proteins in developing, repairing, and regenerating hair cells. Using time-lapse microscopy, we are also studying the dynamic relationship between ion channel clustering and synaptic formation in hair cells and afferent neurons. In future studies, we will determine when hair cell precursors acquire electrical tuning, and, using whole-cell patch-clamp techniques, identify and characterize their L-VGCC and BK currents. We will also use biophysical techniques to determine the number of L-VGCC and BK channels and the size and gating kinetics of their underlying L-VGCC and BK conductances, correlating these variables with the amplitude and frequency of membrane oscillations produced by intracellular current steps. We expect these studies to determine how hair cells regulate ion channel expression to achieve specific physiological responses.

Baird, Richard A.↗
NASA NTRS1990

Electronic neural networks for global optimization

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

Thakoor, A. P.↗