SEARCH · Search NASA
Results for “Memory Formation”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Multiple memory formation in glassy landscapes
An interacting-rearrangement model for sheared solids shows multiple interaction-encoding memories and complex limit cycles.
Effect of directed aging on nonlinear elasticity and memory formation in a material
Disordered solids often change their elastic response as they slowly age. Using experiments and simulations, we study how aging disordered planar networks under an applied stress affects their nonlinear elastic response. We are able to modify dramatically the elastic properties of our systems in the nonlinear regime. Using simulations, we study two models for the microscopic evolution of properties of such a material; the first considers changes in the material strength, while the second considers distortions in the microscopic geometry. Both models capture different aspects of the experiments including the encoding of memories of the aging history of the system and the dramatic effects on the material’s nonlinear elastic properties. Our results demonstrate how aging can be used to create complex elastic behavior in the nonlinear regime.
Design of functional materials based on new principles of disorder. Final Report
Disorder is present to some extent in every material. The disorder can be sparse and localized – individual defects – or globally such as in a glass. Normally disorder is considered to be detrimental to function. However, this is not necessarily always the case. This project addressed the question: How does disorder provide new potentialities for the behavior of a material that would be absent in a crystalline material of the same composition? We have discovered a new principle for disordered matter: Independence of bond-level response. This work has introduced the possibility of creating new classes of mechanical meta-materials with applications everywhere from microscopic biological molecules (allostery) to large scale architectural disordered networks. During the course of this grant, we have subsequently elaborated on these novel ideas about bond-level independence of properties in disordered materials. In particular, this study has required that we understand the limits of disorder. That is, if crystals are perfect order, is there another diametrically opposed pole which is the most disordered state? We have shown that, in a well-defined way, jamming is one extreme disordered pole of rigid matter. Jamming is a far-from-equilibrium way of creating a rigid solid by rapidly quenching a random distribution of particles to a mechanically stable system at zero temperature. To understand the jammed state of matter, we have elucidated the low-frequency excitation of these systems. This has allowed a new way of understanding materials that cannot be easily classified as crystals or glasses. These ideas can be extended to address the mechanical properties of polycrystalline matter. Memory formation in matter can take inspiration from memory that occurs in a biological context. The issue of memory formation raises another set of questions at the heart of how the energy landscape is organized and how it can be manipulated to store information in a solid. This project has explored how to understand the degree to which multiple memories can be stored in a single system and has created a holistic understanding of how inputs can be used to create a desired response.
Odor exposure during imprinting periods increases odorant-specific sensitivity and receptor gene expression in coho salmon ( Oncorhynchus kisutch )
ABSTRACT Pacific salmon are well known for their homing migrations; juvenile salmon learn odors associated with their natal streams prior to seaward migration, and then use these retained odor memories to guide them back from oceanic feeding grounds to their river of origin to spawn several years later. This memory formation, termed olfactory imprinting, involves (at least in part) sensitization of the peripheral olfactory epithelium to specific odorants. We hypothesized that this change in peripheral sensitivity is due to exposure-dependent increases in the expression of odorant receptor (OR) proteins that are activated by specific odorants experienced during imprinting. To test this hypothesis, we exposed juvenile coho salmon, Oncorhynchus kisutch, to the basic amino acid odorant l-arginine during the parr–smolt transformation (PST), when imprinting occurs, and assessed sensitivity of the olfactory epithelium to this and other odorants. We then identified the coho salmon ortholog of a basic amino acid odorant receptor (BAAR) and determined the mRNA expression levels of this receptor and other transcripts representing different classes of OR families. Exposure to l-arginine during the PST resulted in increased sensitivity to that odorant and a specific increase in BAAR mRNA expression in the olfactory epithelium relative to other ORs. These results suggest that specific increases in ORs activated during imprinting may be an important component of home stream memory formation and this phenomenon may ultimately be useful as a marker of successful imprinting to assess management strategies and hatchery practices that may influence straying in salmon.
Molecular pathways for learning in the single-cell Stentor coeruleus
The single-cell Stentor coeruleus contracts in response to mechanical taps but habituates and learns to ignore the taps after repeated stimulation. Here, we explored the molecular changes that occur during the formation of this cellular memory in order to improve our understanding of non-synaptic learning. We impaired cellular protein synthesis with cycloheximide and puromycin and found that, contrary to the effects of such treatments on metazoa, these drugs accelerate habituation and prolong memory retention in Stentor . Exploratory proteomic and transcriptomic analyses identified candidate proteins and genes that changed over the course of habituation and response recovery, pointing toward the regulation of Stentor learning by calcium signaling and protein phosphorylation. Building on these results, we found that using RNA interference to knock down the calcium-binding, EF-hand domain-containing protein SteCoe_6763 accelerated habituation. Furthermore, increased extracellular calcium improved Stentor learning, while treatment with kinase and phosphatase inhibitors impaired learning. In particular, KN-93, a drug known to inhibit calcium/calmodulin-dependent kinase II and voltage-gated calcium channels, decreased both the rate and extent of habituation in Stentor , similar to its effects on learning in metazoa. We also discovered that habituation memory can be maintained in progeny following cell division. Taken together, these results suggest that response recovery in Stentor requires new protein synthesis and that memory formation involves the modification of delocalized mechanoreceptors by phosphorylation and calcium signaling. This is consistent with our previous model of Stentor learning, in which habituation occurs through the inactivation of cell-surface receptors.
Selective consolidation of learning and memory via recall-gated plasticity
In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.
Selective consolidation of learning and memory via recall-gated plasticity
In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.
Microstructural and rheological training and memory of nanocolloidal soft glasses under cyclic shear
An intrinsic feature of disordered and out-of-equilibrium materials, such as glasses, is the dependence of their properties on their history. An important example is rheological memory, in which disordered solids obtain properties based on their deformation history. Here, in this study, we employ x-ray photon correlation spectroscopy with in situ rheometry to characterize memory formation in a nanocolloidal soft glass due to cyclic shear. During a cycle, particles undergo irreversible displacements composed of a combination of shear-induced diffusion and heterogeneous, residual strain fields. At lower shear amplitudes, the displacements resemble a random walk in which the directions in each cycle are independent of those in preceding cycles, while at high amplitude, the irreversible displacements in consecutive cycles become correlated. The magnitudes of the displacements decrease with each cycle before reaching a steady state where the microstructure has been trained to achieve enhanced reversibility even at shear amplitudes well above yielding and despite the presence of thermal fluctuations. At amplitudes below and near yielding, these decreases are monotonic, while well above yielding, they are nonmonotonic, suggesting evidence of shear banding. Accompanying this microstructural training are corresponding decreases in the dissipation during each cycle and the magnitude of the residual stress toward steady-state values. Memory of the training is revealed by measurements in which the amplitude of the shear is changed after steady state is reached. The magnitude of the particle displacements, as well as the dissipation and the change in residual stress, vary nonmonotonically with the new shear amplitude, having minima near the training amplitude, thereby revealing correlated microscopic and macroscopic signatures of memory.
Genetic variations and their interaction with thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice
Thirdhand smoke (THS) is linked to adverse health effects, but the effect of genetic variations on behavioral outcomes is poorly understood. To investigate this, we assessed anxiety- and memory-related behaviors in 820 mice from 21 strains of the genetically diverse Collaborative Cross (CC) mouse that were exposed to THS from 4 through 10 weeks of age. Anxiety was evaluated with a light/dark box assay with a previously established risk score system. Females were generally more sensitive: THS reduced anxiety risk in strains CC013, CC019, and CC051, but increased risk in CC036 and CC061, while males showed no significant effects. Memory was tested using passive avoidance: impairments were observed in both sexes in CC016 and CC019, with sex-dependent effects in CC002 and CC051. A genome-wide association study identified 2,347 SNPs associated with anxiety and 1,568 SNPs with memory, with 32 and 85 SNPs, respectively, interacting with THS exposure. Enrichment analyses revealed distinct biological processes underlying susceptibility, including axonogenesis, synapse organization, cognition, and learning and memory. KEGG pathway analysis identified distinct genetic pathways, including GTPase binding and GTPase regulatory activity, that act as critical molecular switches in the brain that regulate synaptic plasticity, dendritic spine structure, and neuronal signaling, directly influencing anxiety-like behaviors and memory formation. These findings show that THS exposure affects neurobehavioral outcomes in a sex- and genotype-dependent manner, highlighting critical gene-environment interactions and providing a foundation for mechanistic insights into THS neurotoxicity
Structure and mechanism of human vesicular polyamine transporter
Polyamines play essential roles in gene expression and modulate neuronal transmission in mammals. Vesicular polyamine transporters (VPAT) from the SLC18 family exploit the transmembrane H + gradient to translocate polyamines into secretory vesicles, enabling the quantal release of polyamine neuromodulators and underpinning learning and memory formation. Here, we report the cryo-electron microscopy structures of human VPAT in complex with spermine, spermidine, H + , or tetrabenazine, elucidating discrete lumen-facing states of the antiporter and pivotal interactions between VPAT and its substrate or inhibitor. Leveraging structure-inspired mutagenesis studies and protein structure prediction, we deduce an unforeseen mechanism whereby the polyamine and H + compete for multiple acidic protein residues both directly and indirectly, and rationalize how the antidopaminergic therapeutic tetrabenazine impedes vesicular transport of polyamines. This study unravels the mechanism of an H + -coupled polyamine antiporter, reveals mechanistic diversity between VPAT and other SLC18 antiporters, and raises new prospects for combating human disorders of polyamine homeostasis.
Design of functional materials based on new principles of disorder
The main goal of the project for many years was to study and understand the jamming transition and the properties of the marginally jammed state, and to apply that understanding to real systems, including atomic, molecular, metallic and polymeric glasses, colloidal suspensions, granular materials, foams and emulsions, as well as the dynamics of systems driven away from the static jammed state by applying temperature or strain. As we gained greater insight into the jamming problem, we applied it to other big problems that had not previously been recognized as being related to jamming, such as memory formation, metamaterials design and learning.
Loss circulation prevention in geothermal drilling by shape memory polymer
Geothermal formations are naturally fractured with large fracture openings and networks. This can often lead to frequent drilling fluid loss events which is a major contributor to the cost and non-productive time in geothermal drilling. Development of smarter technologies and methods to tackle problem lost circulation is vital for geothermal drilling cost reduction required for geothermal energy to be recognized as a competitive alternative energy source. Mitigation of this problem and other drilling problems such as stuck pipe can minimize overall project cost. In this report, a thermoset shape memory polymer that can be activated by formation natural heat was assessed to seal near wellbore fractures in geothermal wells. The performance of the shape memory polymer (SMP) was evaluated using artificial fractures created in aluminum discs and cylindrical granite cores. Rheology and particle size distribution were considered. A novel testing setup was built for this work, which allows testing of sealing efficiency under dynamic conditions at high temperature. Analysis showed that the SMP has efficiently succeeded in forming a strong plug inside the fractures and stopped fluid loss at high sealing pressure. This smart loss circulation material can expand within the fractures to reduces non-drilling time and strengthen the wellbore in high-temperature drilling operations.
Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels
Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.
Effects of local order parameter dependent transport coefficient in diblock copolymers under applied electric fields
In this work, we present an approach for constructing thermodynamically consistent time-dependent models relevant to thin films of diblock copolymers in applied electric fields. The approach is based on the principles of linear irreversible thermodynamics, and, in this work, it is applied to study the effects of electric fields on thin films of incompressible diblock copolymers. Enforcement of local incompressibility constraint at all times leads to a local order parameter dependent transport coefficient in the model for the diblock copolymers. The dependence of the transport coefficient on the local order parameter is used to relate it with the diffusion constant of Rouse chains and leads to sensitivity of the model to initial conditions. In addition, transient behavior is found to be affected when compared with an ad hoc model assuming a constant transport coefficient. Numerical results such as electric field induced alignment of lamellae domains due to the field are found to be in qualitative agreement with experiments. This approach opens up a systematic way of developing kinetic models for simulating effects of electrolytes added to thin films containing diblock copolymers in the presence of applied electric fields.
California-Kepler Survey. IX. Revisiting the Minimum-mass Extrasolar Nebula with Precise Stellar Parameters
We investigate a possible correlation between the solid surface density Σ of the minimum-mass extrasolar nebula (MMEN) and the host star mass M {sub ⋆} and metallicity [Fe/H]. Leveraging on the precise host star properties from the California-Kepler Survey (CKS), we found that Σ=50{sub −20}{sup +33} g cm{sup −2} (a/1 au){sup −1.75±0.07} (M {sub ⋆}/M {sub ⊙}){sup 1.04±0.22} 10{sup 0.22±0.05[Fe/H]} for Kepler-like systems (1–4R {sub ⊕}; a < 1 au). The strong M {sub ⋆} dependence is reminiscent of previous dust continuum results that the solid disk mass scales with M {sub ⋆}. The weaker [Fe/H] dependence shows that sub-Neptune planets, unlike giant planets, form readily in lower metallicity environment. The innermost region (a < 0.1 au) of an MMEN maintains a smooth profile despite a steep decline of planet occurrence rate: a result that favors the truncation of disks by corotating magnetospheres with a range of rotation periods, rather than the sublimation of dust. The Σ of Kepler multitransiting systems shows a much stronger correlation with M {sub ⋆} and [Fe/H] than singles. This suggests that the dynamically hot evolution that produced single systems also partially removed the memory of formation in disks. Radial-velocity planets yielded a MMEN very similar to CKS planets; transit-timing-variation planets’ postulated convergent migration history is supported by their poorly constrained MMEN. We found that lower mass stars have a higher efficiency of forming/retaining planets: for Sun-like stars, about 20% of the solid mass within ∼1 au are converted/preserved as sub-Neptunes, compared to 70% for late-K to early-M stars. This may be due to the lower binary fraction, lower giant-planet occurrence, or the longer disk lifetime of lower mass stars.
Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels
Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par and Alpaka, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.
Experimental study on rheological and settling properties of shape memory polymer for fracture sealing in geothermal formations
This article studies the rheology and annular flow of a smart lost circulation material (LCM) at 160 °C, using a high-temperature flow loop. The smart LCM is a shape memory polymer (SMP) that activates at high temperatures, and its particle size increases to seal fractures in geothermal formations. SMP was mixed with the base fluid in two different concentrations, 1.0 and 3.0 wt%, to study rheology, wellbore hydraulics, activation process, and settling behavior under different testing conditions. The results of this study showed that the SMP could be activated at high temperatures. An increase of 80–100% in the particle size was observed at 160 °C. The mud samples showed a high shear-thinning behavior at the two concentrations with a Power-law flow index (n) ranging between 0.025 and 0.101. No additional frictional pressure losses were observed when SMP was added to the base fluid. SMP particles showed an excellent suspension at 1.0 wt% while, at 3.0 wt%, a bed was formed at a low flow rate and without pipe rotation. Increasing the drill pipe rotational speed or flow rate effectively removed the bed and homogeneously dispersed the SMP particles, ensuring a better sealing efficiency. SMP particle dispersion in inclined wells was better than in horizontal wells. Moreover, the findings of this study help optimize the lost circulation treatment by considering a wide range of operating parameters that can further be extended to different systems and geometries.