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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

From IMT Device Measurements to Network-Level Consequences: When Learning Suppresses Beyond-LIF Neuron Dynamics

Emerging neuromorphic devices such as insulator--metal transition (IMT) devices exhibit complex temporal dynamics, including slow internal state memory, hysteresis, and burst-like firing, which are poorly captured by conventional leaky integrate-and-fire (LIF) neurons. However, it remains unclear when such dynamics influence learning and inference at the network level, particularly under commonly used unsupervised plasticity rules. We present a controlled, full-stack co-design study spanning experimental characterization of individual IMT devices, compact neuron model development, and large-scale spiking network simulations with identical architectures and learning rules. Rather than optimizing benchmark accuracy, our goal is to diagnose when neuron-level dynamics survive learning and competition, and when they are suppressed, to inform the co-design of devices, networks, and learning rules that can exploit beyond-LIF complexity.

42 ENGINEERING↗
NASA NTRS1994

Neural network with dynamically adaptable neurons

This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

Tawel, Raoul↗
DOE OSTI2024

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2023

Structure of the Sec14 domain of Kalirin reveals a distinct class of lipid-binding module in RhoGEFs

Gated entry of lipophilic ligands into the enclosed hydrophobic pocket in stand-alone Sec14 domain proteins often links lipid metabolism to membrane trafficking. Similar domains occur in multidomain mammalian proteins that activate small GTPases and regulate actin dynamics. The neuronal RhoGEF Kalirin, a central regulator of cytoskeletal dynamics, contains a Sec14 domain (Kal bSec14 ) followed by multiple spectrin-like repeats and catalytic domains. Previous studies demonstrated that Kalirin lacking its Sec14 domain fails to maintain cell morphology or dendritic spine length, yet whether and how Kal bSec14 interacts with lipids remain unknown. Here, we report the structural and biochemical characterization of Kal bSec14 . Kal bSec14 adopts a closed conformation, sealing off the canonical ligand entry site, and instead employs a surface groove to bind a limited set of lysophospholipids. The low-affinity interactions of Kal bSec14 with lysolipids are expected to serve as a general model for the regulation of Rho signaling by other Sec14-containing Rho activators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons

We report on switching dynamics of individual and coupled vanadium dioxide (VO 2 ) devices subject to voltage pulses as the temperature is systematically varied from room temperature spanning the insulator–metal transition (IMT) temperature. The switching voltage of single devices has a strong relationship with both temperature and voltage pulsewidth. Two-step switching in connected VO 2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO 2 artificial neurons was implemented into a spiking neural network (SNN). During training, modulating the switching voltage via temperature affords a novel method to implement homeostasis with the coupled devices. Simulation results show the efficacy of the stochastic neuronal characteristics and the proposed homeostasis mechanism on a standard digit recognition task. As a result, these studies contribute to ongoing efforts in neuromorphic computing exploiting collective phase transitions.

42 ENGINEERING↗
DOE OSTI2023

Attention for Causal Relationship Discovery from Biological Neural Dynamics

This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks. Our study primarily focuses on a proof-of-concept investigation based on simulated neural dynamics, for which the ground-truth causality is known through the underlying connectivity matrix. For transformer models trained to forecast neuronal population dynamics, we show that the cross-attention module effectively captures the causal relationship among neurons, with an accuracy equal to or superior to that of the most popular Granger causality discovery method. While we acknowledge that real-world neurobiology data will bring further challenges, including dynamic connectivity and unobserved variability, this research offers an encouraging preliminary glimpse into the utility of the transformer model for causal representation learning in neuroscience.

Lu, Ziyu↗
DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗
DOE OSTI2023

Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation

Intracortical microstimulation (ICMS) has been used for the development of brain machine interfaces. However, further understanding about the spatiotemporal responses of neurons to different electrical stimulation parameters is necessary to inform the design of optimal therapies. In this study, we employed in vivo electrophysiological recording, two-photon calcium imaging, and electric field simulation to evaluate the acute effect of ICMS on layer II/III neurons. Our results show that stimulation frequency non-linearly modulates neuronal responses, whereas the magnitude of responses is linearly correlated to the electric field strength and stimulation amplitude before reaching a steady state. Temporal dynamics of neurons’ responses depends more on stimulation frequency and their distance to the stimulation electrode. In addition, amplitude-dependent post-stimulation suppression was observed within ~500 μm of the stimulation electrode, as evidenced by both calcium imaging and local field potentials. These findings provide insights for selecting stimulation parameters to achieve desirable spatiotemporal specificity of ICMS.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Adding dynamic rules to self-organizing fuzzy systems

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

Buhusi, Catalin V.↗
NASA NTRS1994

Computational Approaches to Vestibular Research

The Biocomputation Center at NASA Ames Research Center is dedicated to a union between computational, experimental and theoretical approaches to the study of neuroscience and of life sciences in general. The current emphasis is on computer reconstruction and visualization of vestibular macular architecture in three-dimensions (3-D), and on mathematical modeling and computer simulation of neural activity in the functioning system. Our methods are being used to interpret the influence of spaceflight on mammalian vestibular maculas in a model system, that of the adult Sprague-Dawley rat. More than twenty 3-D reconstructions of type I and type II hair cells and their afferents have been completed by digitization of contours traced from serial sections photographed in a transmission electron microscope. This labor-intensive method has now been replace d by a semiautomated method developed in the Biocomputation Center in which conventional photography is eliminated. All viewing, storage and manipulation of original data is done using Silicon Graphics workstations. Recent improvements to the software include a new mesh generation method for connecting contours. This method will permit the investigator to describe any surface, regardless of complexity, including highly branched structures such as are routinely found in neurons. This same mesh can be used for 3-D, finite volume simulation of synapse activation and voltage spread on neuronal surfaces visualized via the reconstruction process. These simulations help the investigator interpret the relationship between neuroarchitecture and physiology, and are of assistance in determining which experiments will best test theoretical interpretations. Data are also used to develop abstract, 3-D models that dynamically display neuronal activity ongoing in the system. Finally, the same data can be used to visualize the neural tissue in a virtual environment. Our exhibit will depict capabilities of our computational approaches and some of our findings from their application. For example, our research has demonstrated that maculas of adult mammals retain the property of synaptic plasticity. Ribbon synapses increase numerically and undergo changes in type and distribution (p<0.0001) in type II hair cells after exposure to microgravity for as few as nine days. The finding of macular synaptic plasticity is pertinent to the clinic, and may help explain some. balance disorders in humans. The software used in our investigations will be demonstrated for those interested in applying it in their own research.

Ross, Muriel D.↗
DOE OSTI2025

PCM1 coordinates centrosome asymmetry with polarized endosome dynamics to regulate daughter cell fate

Vertebrate radial glia progenitors (RGPs) balance self-renewal and differentiation through asymmetric cell division (ACD), which involves unequal centrosome inheritance. How centrosome asymmetry directs cell fate remains poorly understood. Here, we identify Pericentriolar material 1 (Pcm1) as a key player in this process. In zebrafish embryonic RGPs, Pcm1 is asymmetrically associated with Cep83, a mother centrosome marker. Using in vivo time-lapse imaging and nanoscale-resolution expansion microscopy, we detect Pcm1 on Notch ligand-containing endosomes, where it interacts–either directly or indirectly–with Par-3 and dynein. Loss of pcm1 disrupts endosome dynamics, increasing neuronal differentiation at the expense of RGP self-renewal. Mechanistically, Pcm1 facilitates the transition from Rab5b to Rab11a and promotes the assembly of Par-3 and dynein macromolecular complexes on recycling endosomes. Furthermore, we find conserved PARD3-PCM1-CEP83-RAB11 associations in human cortical brain organoids. Our findings uncover that Pcm1 links centrosome asymmetry to polarized endosome trafficking, thereby regulating RGP fate decisions.

Cell fate and cell lineage↗
DOE OSTI2024

Systems and methods for dynamically reconfigurable artificial synapses and neurons with tunable activation functions

An ionic redox transistor comprises a solid channel, a solid reservoir layer, and a solid electrolyte layer disposed between the channel and the reservoir layer. The channel exhibits a substantially linear current-voltage relationship in a first range of voltages, and a nonlinear current-voltage relationship in a second range of voltages that is greater than the first range of voltages. One or both of the substantially linear current-voltage relationship or the nonlinear current-voltage relationship of the channel is varied by changing the concentration of ions such as oxygen vacancies in the channel. Ion or vacancy transport between the channel and the reservoir layer across the electrolyte layer occurs in response to applying a voltage between the channel and the reservoir layer. Subject to the first range of voltages, the channel can function as a synapse device. Subject to the second range of voltages, the channel can function as a neuron device.

Talin, Albert Alec↗
DOE OSTI2024

Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning

With significant improvements in large-scale simulations of brain models, there is a growing need to develop tools for rapid analysis and interpreting the simulation results. In this work, we explore the potential of sequential deep learning models to understand and explain the network dynamics among the neurons extracted from a large-scale neural simulation in STACS (Simulation Tool for Asynchronous Cortical Stream). Our method employs a representative neuroscience model that abstracts the cortical dynamics with a reservoir of randomly connected spiking neurons with a low stable spike firing rate throughout the simulation duration. We subsequently analyze the spike dynamics of the simulated spiking neural network through an autoencoder model and an attention-based mechanism.

Kulkarni, Shruti↗
NASA NTRS1998

The adequate stimulus for avian short latency vestibular responses to linear translation

Transient linear acceleration stimuli have been shown to elicit eighth nerve vestibular compound action potentials in birds and mammals. The present study was undertaken to better define the nature of the adequate stimulus for neurons generating the response in the chicken (Gallus domesticus). In particular, the study evaluated the question of whether the neurons studied are most sensitive to the maximum level of linear acceleration achieved or to the rate of change in acceleration (da/dt, or jerk). To do this, vestibular response thresholds were measured as a function of stimulus onset slope. Traditional computer signal averaging was used to record responses to pulsed linear acceleration stimuli. Stimulus onset slope was systematically varied. Acceleration thresholds decreased with increasing stimulus onset slope (decreasing stimulus rise time). When stimuli were expressed in units of jerk (g/ms), thresholds were virtually constant for all stimulus rise times. Moreover, stimuli having identical jerk magnitudes but widely varying peak acceleration levels produced virtually identical responses. Vestibular response thresholds, latencies and amplitudes appear to be determined strictly by stimulus jerk magnitudes. Stimulus attributes such as peak acceleration or rise time alone do not provide sufficient information to predict response parameter quantities. Indeed, the major response parameters were shown to be virtually independent of peak acceleration levels or rise time when these stimulus features were isolated and considered separately. It is concluded that the neurons generating short latency vestibular evoked potentials do so as "jerk encoders" in the chicken. Primary afferents classified as "irregular", and which traditionally fall into the broad category of "dynamic" or "phasic" neurons, would seem to be the most likely candidates for the neural generators of short latency vestibular compound action potentials.

NASA Discipline Neuroscience↗
DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Photothermally Activated Artificial Neuromorphic Synapses

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. Here, to incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO 2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to “activate” these neurons and store “memory” by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2000

Spatiotemporal processing of linear acceleration: primary afferent and central vestibular neuron responses

Spatiotemporal convergence and two-dimensional (2-D) neural tuning have been proposed as a major neural mechanism in the signal processing of linear acceleration. To examine this hypothesis, we studied the firing properties of primary otolith afferents and central otolith neurons that respond exclusively to horizontal linear accelerations of the head (0.16-10 Hz) in alert rhesus monkeys. Unlike primary afferents, the majority of central otolith neurons exhibited 2-D spatial tuning to linear acceleration. As a result, central otolith dynamics vary as a function of movement direction. During movement along the maximum sensitivity direction, the dynamics of all central otolith neurons differed significantly from those observed for the primary afferent population. Specifically at low frequencies (</=0.5 Hz), the firing rate of the majority of central otolith neurons peaked in phase with linear velocity, in contrast to primary afferents that peaked in phase with linear acceleration. At least three different groups of central response dynamics were described according to the properties observed for motion along the maximum sensitivity direction. "High-pass" neurons exhibited increasing gains and phase values as a function of frequency. "Flat" neurons were characterized by relatively flat gains and constant phase lags (approximately 20-55 degrees ). A few neurons ("low-pass") were characterized by decreasing gain and phase as a function of frequency. The response dynamics of central otolith neurons suggest that the approximately 90 degrees phase lags observed at low frequencies are not the result of a neural integration but rather the effect of nonminimum phase behavior, which could arise at least partly through spatiotemporal convergence. Neither afferent nor central otolith neurons discriminated between gravitational and inertial components of linear acceleration. Thus response sensitivity was indistinguishable during 0.5-Hz pitch oscillations and fore-aft movements. The fact that otolith-only central neurons with "high-pass" filter properties exhibit semicircular canal-like dynamics during head tilts might have important consequences for the conclusions of previous studies of sensory convergence and sensorimotor transformations in central vestibular neurons.

NASA Discipline Neuroscience↗