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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Temporally selective contextual encoding in the dentate gyrus of the hippocampus

A recent model of the hippocampus predicts that the unique properties of the dentate gyrus allow for temporal separation of events. This temporal separation is accomplished in part through the continual generation of new neurons, which, due to a transient window of hyperexcitability, could allow for preferential encoding of information present during their development. Here we obtain in vivo electrophysiological recordings and identify a cell population exhibiting activity that is selective to single contexts when rats experience a long temporal separation between context exposures during training. This selectivity is attenuated as the temporal separation between context exposures is shortened and is further attenuated when neurogenesis is reduced. Our data reveal the existence of a temporal orthogonalizing neuronal code within the dentate gyrus, a hallmark feature of episodic memory.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Small footprint optoelectrodes using ring resonators for passive light localization

The combination of electrophysiology and optogenetics enables the exploration of how the brain operates down to a single neuron and its network activity. Neural probes are in vivo invasive devices that integrate sensors and stimulation sites to record and manipulate neuronal activity with high spatiotemporal resolution. State-of-the-art probes are limited by tradeoffs involving their lateral dimension, number of sensors, and ability to access independent stimulation sites. Here, we realize a highly scalable probe that features three-dimensional integration of small-footprint arrays of sensors and nanophotonic circuits to scale the density of sensors per cross-section by one order of magnitude with respect to state-of-the-art devices. For the first time, we overcome the spatial limit of the nanophotonic circuit by coupling only one waveguide to numerous optical ring resonators as passive nanophotonic switches. With this strategy, we achieve accurate on-demand light localization while avoiding spatially demanding bundles of waveguides and demonstrate the feasibility with a proof-of-concept device and its scalability towards high-resolution and low-damage neural optoelectrodes.

42 ENGINEERING↗
DOE OSTI2022

Selective posttranslational inhibition of Ca V β 1 -associated voltage-dependent calcium channels with a functionalized nanobody

Ca 2+ influx through high-voltage-activated calcium channels (HVACCs) controls diverse cellular functions. A critical feature enabling a singular signal, Ca 2+ influx, to mediate disparate functions is diversity of HVACC pore-forming α1 and auxiliary Ca V β 1 –Ca V β 4 subunits. Selective Ca V α 1 blockers have enabled deciphering their unique physiological roles. By contrast, the capacity to post-translationally inhibit HVACCs based on Ca V β isoform is non-existent. Conventional gene knockout/shRNA approaches do not adequately address this deficit owing to subunit reshuffling and partially overlapping functions of Ca V β isoforms. Here, we identify a nanobody (nb.E8) that selectively binds Ca V β 1 SH3 domain and inhibits Ca V β 1 -associated HVACCs by reducing channel surface density, decreasing open probability, and speeding inactivation. Functionalizing nb.E8 with Nedd4L HECT domain yielded Chisel-1 which eliminated current through Ca V β 1 -reconstituted Ca V 1/Ca V 2 and native Ca V 1.1 channels in skeletal muscle, strongly suppressed depolarization-evoked Ca 2+ influx and excitation-transcription coupling in hippocampal neurons, but was inert against Ca V β 2 -associated Ca V 1.2 in cardiomyocytes. The results introduce an original method for probing distinctive functions of ion channel auxiliary subunit isoforms, reveal additional dimensions of Ca V β 1 signaling in neurons, and describe a genetically-encoded HVACC inhibitor with unique properties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Collective dynamics and long-range order in thermal neuristor networks

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Science & Technology - Other Topics↗
DOE OSTI2025

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

bio-inspired computing↗
DOE OSTI2015

Radiant energy required for infrared neural stimulation

Infrared neural stimulation (INS) has been proposed as an alternative method to electrical stimulation because of its spatial selective stimulation. Independent of the mechanism for INS, to translate the method into a device it is important to determine the energy for stimulation required at the target structure. Custom-designed, flat and angle polished fibers, were used to deliver the photons. By rotating the angle polished fibers, the orientation of the radiation beam in the cochlea could be changed. INS-evoked compound action potentials and single unit responses in the central nucleus of the inferior colliculus (ICC) were recorded. X-ray computed tomography was used to determine the orientation of the optical fiber. Maximum responses were observed when the radiation beam was directed towards the spiral ganglion neurons (SGNs), whereas little responses were seen when the beam was directed towards the basilar membrane. The radiant exposure required at the SGNs to evoke compound action potentials (CAPs) or ICC responses was on average 18.9 ± 12.2 or 10.3 ± 4.9 mJ/cm 2 , respectively. For cochlear INS it has been debated whether the radiation directly stimulates the SGNs or evokes a photoacoustic effect. The results support the view that a direct interaction between neurons and radiation dominates the response to INS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter

Here, the dopamine transporter (DAT) belongs to the neurotransmitter:sodium symporter (NSS) family of membrane proteins that are responsible for reuptake of neurotransmitters from the synaptic cleft to terminate a neuronal signal and enable subsequent neurotransmitter release from the presynaptic neuron. The release of one sodium ion from the crystallographically determined sodium binding site Na2 had been identified as an initial step in the transport cycle which prepares the transporter for substrate translocation by stabilizing an inward-open conformation. We have constructed Markov State Models (MSMs) from extensive molecular dynamics simulations of human DAT (hDAT) to explore the mechanism of this sodium release. Our results quantify the release process triggered by hydration of the Na2 site that occurs concomitantly with a conformational transition from an outward-facing to an inward-facing state of the transporter. The kinetics of the release process are computed from the MSM, and transition path theory is used to identify the most probable sodium release pathways. An intermediate state is discovered on the sodium release pathway, and the results reveal the importance of various modes of interaction of the N-terminus of hDAT in controlling the pathways of release.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

How to induce multiple delays in coupled chaotic oscillators?

Lag synchronization is a basic phenomenon in mismatched coupled systems, delay coupled systems, and time-delayed systems. It is characterized by a lag configuration that identifies a unique time shift between all pairs of similar state variables of the coupled systems. In this report, an attempt is made how to induce multiple lag configurations in coupled systems when different pairs of state variables attain different time shift. A design of coupling is presented to realize this multiple lag synchronization. Numerical illustration is given using examples of the Rössler system and the slow-fast Hindmarsh-Rose neuron model. The multiple lag scenario is physically realized in an electronic circuit of two Sprott systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

Rhythmic neuronal oscillations across a broad range of frequencies, as well as spatiotemporal phenomena, such as waves and bumps, have been observed in various areas of the brain and proposed as critical to brain function. While there is a long and distinguished history of studying rhythms in nerve cells and neuronal networks in healthy organisms, the association and analysis of rhythms to diseases are more recent developments. Indeed, it is now thought that certain aspects of diseases of the nervous system, such as epilepsy, schizophrenia, Parkinson's, and sleep disorders, are associated with transitions or disruptions of neurological rhythms. This focus issue brings together articles presenting modeling, computational, analytical, and experimental perspectives about rhythms and dynamic transitions between them that are associated to various diseases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential

A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Reparative neurogenesis after cerebral ischemia: Clinical application prospects

At the present time two main approaches are in the focus of neurobiological studies of brain recovery after a stroke. One of them is concerned with the infusion of stem cells in damaged brain. The second approach is directed at the stimulation of endogenous reparative processes, in particular, adult neurogenesis. This review considers alterations of adult neurogenesis caused by cerebral ischemia and possible pathways of its regulation. Multiple studies on animal models have shown that adult neurogenesis is mostly increased by cerebral ischemia. In spite of increasing proliferation and moving neural progenitors to infarct zone, most newborn neurons die before reaching maturity. Besides, an increase of neurogenesis in pathological conditions is mainly due to recruitment of new stem cells, but not due to an additional precursor-cells division that results in an overall decline of the regeneration capacity. Thus, the endogenous reparative mechanisms are not sufficient, and the search for new targets to promote proliferation, survival, and maturation of new neurons after a stroke is needed. Neurotransmitter systems and anti-inflammatory drugs are considered as potential regulators of post-ischemic neurogenesis growth factors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Organismic materials for beyond von Neumann machines

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO 2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. Finally, we conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.

42 ENGINEERING↗
DOE OSTI2013

Syntaxin1a variants lacking an N-peptide or bearing the LE mutation bind to Munc18a in a closed conformation

In neurons, soluble N-ethylmaleimide–sensitive factor attachment receptor (SNARE) proteins drive the fusion of synaptic vesicles to the plasma membrane through the formation of a four-helix SNARE complex. Members of the Sec1/Munc18 protein family regulate membrane fusion through interactions with the syntaxin family of SNARE proteins. The neuronal protein Munc18a interacts with a closed conformation of the SNARE protein syntaxin1a (Syx1a) and with an assembled SNARE complex containing Syx1a in an open conformation. The N-peptide of Syx1a (amino acids 1–24) has been implicated in the transition of Munc18a-bound Syx1a to Munc18a-bound SNARE complex, but the underlying mechanism is not understood. In addition, we report the X-ray crystal structures of Munc18a bound to Syx1a with and without its native N-peptide (Syx1aΔN), along with small-angle X-ray scattering (SAXS) data for Munc18a bound to Syx1a, Syx1aΔN, and Syx1a L165A/E166A (LE), a mutation thought to render Syx1a in a constitutively open conformation. We show that all three complexes adopt the same global structure, in which Munc18a binds a closed conformation of Syx1a. We also identify a possible structural connection between the Syx1a N-peptide and SNARE domain that might be important for the transition of closed-to-open Syx1a in SNARE complex assembly. Although the role of the N-peptide in Munc18a-mediated SNARE complex assembly remains unclear, our results demonstrate that the N-peptide and LE mutation have no effect on the global conformation of the Munc18a–Syx1a complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Atomic structure of a toxic, oligomeric segment of SOD1 linked to amyotrophic lateral sclerosis (ALS)

Fibrils and oligomers are the aggregated protein agents of neuronal dysfunction in ALS diseases. Whereas we now know much about fibril architecture, atomic structures of disease-related oligomers have eluded determination. Here, we determine the corkscrew-like structure of a cytotoxic segment of superoxide dismutase 1 (SOD1) in its oligomeric state. Mutations that prevent formation of this structure eliminate cytotoxicity of the segment in isolation as well as cytotoxicity of the ALS-linked mutants of SOD1 in primary motor neurons and in a Danio rerio (zebrafish) model of ALS. Cytotoxicity assays suggest that toxicity is a property of soluble oligomers, and not large insoluble aggregates. Here, our work adds to evidence that the toxic oligomeric entities in protein aggregation diseases contain antiparallel, out-of-register β-sheet structures and identifies a target for structure-based therapeutics in ALS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

GHB analogs confer neuroprotection through specific interaction with the CaMKIIα hub domain

Ca 2+ /calmodulin-dependent protein kinase II alpha subunit (CaMKIIα) is a key neuronal signaling protein and an emerging drug target. The central hub domain regulates the activity of CaMKIIα by organizing the holoenzyme complex into functional oligomers, yet pharmacological modulation of the hub domain has never been demonstrated. Here, using a combination of photoaffinity labeling and chemical proteomics, we show that compounds related to the natural substance γ-hydroxybutyrate (GHB) bind selectively to CaMKIIα. By means of a 2.2-Å x-ray crystal structure of ligand-bound CaMKIIα hub, we reveal the molecular details of the binding site deep within the hub. Furthermore, we show that binding of GHB and related analogs to this site promotes concentration-dependent increases in hub thermal stability believed to alter holoenzyme functionality. Selectively under states of pathological CaMKIIα activation, hub ligands provide a significant and sustained neuroprotection, which is both time and dose dependent. This is demonstrated in neurons exposed to excitotoxicity and in a mouse model of cerebral ischemia with the selective GHB analog, HOCPCA (3-hydroxycyclopent-1-enecarboxylic acid). Together, our results indicate a hitherto unknown mechanism for neuroprotection by a highly specific and unforeseen interaction between the CaMKIIα hub domain and small molecule brain-penetrant GHB analogs. This establishes GHB analogs as powerful tools for investigating CaMKII neuropharmacology in general and as potential therapeutic compounds for cerebral ischemia in particular.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2024

Psychosocial experiences are associated with human brain mitochondrial biology

Psychosocial experiences affect brain health and aging trajectories, but the molecular pathways underlying these associations remain unclear. Normal brain function relies on energy transformation by mitochondria oxidative phosphorylation (OxPhos). Two main lines of evidence position mitochondria both as targets and drivers of psychosocial experiences. On the one hand, chronic stress exposure and mood states may alter multiple aspects of mitochondrial biology; on the other hand, functional variations in mitochondrial OxPhos capacity may alter social behavior, stress reactivity, and mood. But are psychosocial exposures and subjective experiences linked to mitochondrial biology in the human brain? By combining longitudinal antemortem assessments of psychosocial factors with postmortem brain (dorsolateral prefrontal cortex) proteomics in older adults, we find that higher well-being is linked to greater abundance of the mitochondrial OxPhos machinery, whereas higher negative mood is linked to lower OxPhos protein content. Combined, positive and negative psychosocial factors explained 18 to 25% of the variance in the abundance of OxPhos complex I, the primary biochemical entry point that energizes brain mitochondria. Moreover, interrogating mitochondrial psychobiological associations in specific neuronal and nonneuronal brain cells with single-nucleus RNA sequencing (RNA-seq) revealed strong cell-type-specific associations for positive psychosocial experiences and mitochondria in glia but opposite associations in neurons. As a result, these “mind-mitochondria” associations were masked in bulk RNA-seq, highlighting the likely underestimation of true psychobiological effect sizes in bulk brain tissues. Thus, self-reported psychosocial experiences are linked to human brain mitochondrial phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗