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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
NASA NTRS1990

Identification of the connections in biologically inspired neural networks

We developed an identification method to find the strength of the connections between neurons from their behavior in small biologically-inspired artificial neural networks. That is, given the network external inputs and the temporal firing pattern of the neurons, we can calculate a solution for the strengths of the connections between neurons and the initial neuron activations if a solution exists. The method determines directly if there is a solution to a particular neural network problem. No training of the network is required. It should be noted that this is a first pass at the solution of a difficult problem. The neuron and network models chosen are related to biology but do not contain all of its complexities, some of which we hope to add to the model in future work. A variety of new results have been obtained. First, the method has been tailored to produce connection weight matrix solutions for networks with important features of biological neural (bioneural) networks. Second, a computationally efficient method of finding a robust central solution has been developed. This later method also enables us to find the most consistent solution in the presence of noisy data. Prospects of applying our method to identify bioneural network connections are exciting because such connections are almost impossible to measure in the laboratory. Knowledge of such connections would facilitate an understanding of bioneural networks and would allow the construction of the electronic counterparts of bioneural networks on very large scale integrated (VLSI) circuits.

Demuth, H.↗
NASA NTRS1994

Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

Tawel, Raoul↗
NASA NTRS1995

Microgravity and Charge Transfer in the Neuronal Membrane: Implications for Computational Neurobiology

Evidence from natural and artificial membranes indicates that the neural membrane is a liquid crystal. A liquid-to-gel phase transition caused by the application of superposed electromagnetic fields to the outer membrane surface releases spin-correlated electron pairs which propagate through a charge transfer complex. The propagation generates Rydberg atoms in the lipid bilayer lattice. In the present model, charge density configurations in promoted orbitals interact as cellular automata and perform computations in Hilbert space. Due to the small binding energies of promoted orbitals, their automata are highly sensitive to microgravitational perturbations. It is proposed that spacetime is classical on the Rydberg scale, but formed of contiguous moving segments, each of which displays topological equivalence. This stochasticity is reflected in randomized Riemannian tensor values. Spacetime segments interact with charge automata as components of a computational process. At the termination of the algorithm, an orbital of high probability density is embedded in a more stabilized microscopic spacetime. This state permits the opening of an ion channel and the conversion of a quantum algorithm into a macroscopic frequency code.

Wallace, Ron↗
DOE OSTI2022

MicroRNA expression in animal models of amyotrophic lateral sclerosis and potential therapeutic approaches

A review of recent animal models of amyotrophic lateral sclerosis showed a large number of miRNAs had altered levels of expression in the brain and spinal cord, motor neurons of spinal cord and brainstem, and hypoglossal, facial, and red motor nuclei and were mostly upregulated. Among the miRNAs found to be upregulated in two of the studies were miR-21, miR-155, miR-125b, miR-146a, miR-124, miR-9, and miR-19b, while those downregulated in two of the studies included miR-146a, miR-29, miR-9, and miR-125b. A change of direction in miRNA expression occurred in some tissues when compared (e.g., miR-29b-3p in cerebellum and spinal cord of wobbler mice at 40 days), or at different disease stages (e.g., miR-200a in spinal cord of SOD1(G93A) mice at 95 days vs. 108 and 112 days). In the animal models, suppression of miR-129-5p resulted in increased lifespan, improved muscle strength, reduced neuromuscular junction degeneration, and tended to improve motor neuron survival in the SOD1(G93A) mouse model. Suppression of miR-155 was also associated with increased lifespan, while lowering of miR-29a tended to improve lifespan in males and increase muscle strength in SOD1(G93A) mice. Overexpression of members of miR-17~92 cluster improved motor neuron survival in SOD1(G93A) mice. Treatment with an artificial miRNA designed to target hSOD1 increased lifespan and improved muscle strength in SOD1(G93A) animals. Further studies with animal models of amyotrophic lateral sclerosis are warranted to validate these findings and identify specific miRNAs whose suppression or directed against hSOD1 results in increased lifespan, improved muscle strength, reduced neuromuscular junction degeneration, and improved motor neuron survival in SOD1(G93A) animals.

59 BASIC BIOLOGICAL SCIENCES↗
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 NTRS1987

Control of abdominal and expiratory intercostal muscle activity during vomiting - Role of ventral respiratory group expiratory neurons

The role of ventral respiratory group (VRG) expiratory (E) neurons in the control of abdominal and internal intercostal muscle activity during vomiting was investigated in cats. Two series of experiments were performed: in one, the activity of VRG E neurons was recorded during fictive vomiting in cats that were decerebrated, paralyzed, and artificially ventilated; in the second, the abdominal muscle activity during vomiting was compared before and after sectioning the axons of descending VRG E neurons in decerebrate spontaneously breathing cats. The results show that about two-thirds of VRG E neurons that project at least as far caudally as the lower thoracic cord contribute to internal intercostal muscle activity during vomiting. The remaining VRG E neurons contribute to abdominal muscle activation. As shown by severing the axons of the VRG E neurons, other, as yet unidenified, inputs (either descending from the brain stem or arising from spinal reflexes) can also produce abdominal muscle activation.

Miller, Alan D.↗
DOE OSTI2025

An electro-optical Mott neuron based on niobium dioxide

Various applications—including brain-like computing and on-chip artificial vision—increasingly demand a combination of electronic and photonic techniques. However, integrating both approaches on a single chip is challenging, and solutions typically rely on disparate components with power-hungry signal conversions. Here, in this paper, we report electro-optical Mott neurons that combine visible light emission with electrical threshold switching, as well as neuron-like oscillations. The devices are based on thin films of sputtered niobium dioxide (NbO 2 ), a Mott insulator–metal transition material, operating at room temperature and emitting light that peaks around 810 nm. Operando measurements reveal an electronic origin to the light emission: charge carrier relaxation initiated by high-field transport in the NbO 2 . Our devices combine electrical and optical functions within a single material, thereby expanding the options available for future artificial intelligence hardware.

electrical engineering↗
DOE OSTI2022

Biological underpinnings for lifelong learning machines

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this Perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence. It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

Kudithipudi, Dhireesha↗
NASA NTRS1990

Neural Network Development Tool (NETS)

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

Baffes, Paul T.↗
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 OSTI2026

Operando microscopy for neuromorphic hardware

Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. Here, this Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1991

High-Gain AlxGa1-xAs/GaAs Transistors For Neural Networks

High-gain AlxGa1-xAs/GaAs npn double heterojunction bipolar transistors developed for use as phototransistors in optoelectronic integrated circuits, especially in artificial neural networks. Transistors perform both photodetection and saturating-amplification functions of neurons. Good candidates for such application because structurally compatible with laser diodes and light-emitting diodes, detect light, and provide high current gain needed to compensate for losses in holographic optical elements.

Kim, Jae-Hoon↗
DOE OSTI2025

Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

generative AI↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
NASA NTRS1992

Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters.

Troudet, T.↗
NASA NTRS1996

Modular, Hierarchical Learning By Artificial Neural Networks

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

Baldi, Pierre F.↗
DOE OSTI2024

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CryoET reveals organelle phenotypes in huntington disease patient iPSC-derived and mouse primary neurons

Huntington’s disease (HD) is caused by an expanded CAG repeat in the huntingtin gene, yielding a Huntingtin protein with an expanded polyglutamine tract. While experiments with patient-derived induced pluripotent stem cells (iPSCs) can help understand disease, defining pathological biomarkers remains challenging. Here, we used cryogenic electron tomography to visualize neurites in HD patient iPSC-derived neurons with varying CAG repeats, and primary cortical neurons from BACHD, deltaN17-BACHD, and wild-type mice. In HD models, we discovered sheet aggregates in double membrane-bound organelles, and mitochondria with distorted cristae and enlarged granules, likely mitochondrial RNA granules. We used artificial intelligence to quantify mitochondrial granules, and proteomics experiments reveal differential protein content in isolated HD mitochondria. Knockdown of Protein Inhibitor of Activated STAT1 ameliorated aberrant phenotypes in iPSC- and BACHD neurons. We show that integrated ultrastructural and proteomic approaches may uncover early HD phenotypes to accelerate diagnostics and the development of targeted therapeutics for HD.

59 BASIC BIOLOGICAL SCIENCES↗