Advanced Memory Research Articles

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses.

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

A Robust numerical technique based on the chromatic polynomials for the European options regulated by the time-fractional Black–Scholes equation

AbstractRisk mitigation and control are critical for investors in the finance sector. Purchasing significant instruments that eliminate the risk of price fluctuation helps investors manage these risks. In theory and practice, option pricing is a substantial issue among many financial derivatives. In this scenario, most investors adopt the Black–Scholes model to describe the behavior of the underlying asset in option pricing. The exceptional memory effect prevalent in fractional derivatives makes it easy to understand and explain the approximation of financial options in terms of their inherited characteristics prompted by the given reason. Finding numerical solutions that are both successful and suitably precise is crucial when working with financial fractional differential equations. Hence, this paper proposes an innovative method, designated the Chromatic polynomial collocation method (CPM), for the theoretical study of the Time fractional Black–Scholes equation (TFBSE) that regulates European call options. The newly developed numerical algorithm CPM is on a functional basis of the Chromatic polynomials of Complete graphs (Kn) and operational matrices of the basis polynomials. The CPM transforms the TFBSE into a framework of nonlinear algebraic equations with the help of operational matrices and equispaced collocation points. The fractional orders in the PDE are concerned in the Caputo sense. The CPM findings further corroborate the results of the most recent numerical schemes to show the effectiveness of the suggested numerical algorithm.

Enhanced Electrical Polarization in van der Waals α-In2Se3 Ferroelectric Semiconductor Field-Effect Transistors by Eliminating Surface Screening Charge.

A van der Waals (vdW) α-In2Se3 ferroelectric semiconductor channel-based field-effect transistor (FeS-FET) has emerged as a next-generation electronic device owing to its versatility in various fields, including neuromorphic computing, nonvolatile memory, and optoelectronics. However, screening charges cause by the imperfect surface morphology of vdW α-In2Se3 inhibiting electrical polarization remain an unresolved issue. In this study, for the first time, a method is elucidated to recover the inherent electric polarization in both in- and out-of-plane directions of the α-In2Se3 channel based on post-exfoliation annealing (PEA) and improve the electrical performance of vdW FeS-FETs. Following PEA, an ultra-thin In2Se3-3xO3x layer formed on the top surface of the α-In2Se3 channel is demonstrated to passivate surface defects and enhance the electrical performance of FeS-FETs. The on/off current ratio of the α-In2Se3 FeS-FET has increased from 5.99 to 1.84 × 106, and the magnitude of ferroelectric resistance switching has increased from 1.20 to 26.01. Moreover, the gate-modulated artificial synaptic operation of the α-In2Se3 FeS-FET is demonstrated and illustrate the significance of the engineered interface in the vdW FeS-FET for its application to multifunctional devices. The proposed α-In2Se3 FeS-FET is expected to provide a significant breakthrough for advanced memory devices and neuromorphic computing.

Advances in the Differentiation of hiPSCs into Cerebellar Neuronal Cells.

The cerebellum has historically been primarily associated with the regulation of precise motor functions. However, recent findings suggest that it also plays a pivotal role in the development of advanced cognitive functions, including learning, memory, and emotion regulation. Pathological changes in the cerebellum, whether congenital hereditary or acquired degenerative, can result in a diverse spectrum of disorders, ranging from genetic spinocerebellar ataxias to psychiatric conditions such as autism, and schizophrenia. While studies in animal models have significantly contributed to our understanding of the genetic networks governing cerebellar development, it is important to note that the human cerebellum follows a protracted developmental timeline compared to the neocortex. Consequently, employing animal models to uncover human-specific molecular events in cerebellar development presents significant challenges. The emergence of human induced pluripotent stem cells (hiPSCs) has provided an invaluable tool for creating human-based culture systems, enabling the modeling and analysis of cerebellar physiology and pathology. hiPSCs and their differentiated progenies can be derived from patients with specific disorders or carrying distinct genetic variants. Importantly, they preserve the unique genetic signatures of the individuals from whom they originate, allowing for the elucidation of human-specific molecular and cellular processes involved in cerebellar development and related disorders. This review focuses on the technical advancements in the utilization of hiPSCs for the generation of both 2D cerebellar neuronal cells and 3D cerebellar organoids.

Generative adversarial networks for stack voltage degradation and RUL estimation in PEMFCs under static and dynamic loads

Accurately predicting the degradation and remaining useful life (RUL) of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in automotive and energy sectors. Traditional models often fail to capture the complex, non-linear degradation patterns of PEMFCs, resulting in suboptimal predictions. This study addresses these technological gaps by introducing a novel approach, the Self-Attention Temporal Dual Discriminator Generative Adversarial Network (SAT-DD-GAN). This model is designed to overcome the limitations of existing methods by integrating advanced Long Short-Term Memory (LSTM) networks for time-series analysis, a self-attention mechanism to focus on critical degradation signals, and dual discriminators to improve prediction accuracy and robustness. By addressing the gap in handling long-term dependencies and complex degradation patterns, the proposed SAT-DD-GAN bridges the disconnect between traditional models and the real-world degradation behavior of PEMFCs. The SAT-DD-GAN achieved groundbreaking predictive performance, with the lowest RMSE (0.983E-3 for static load, 1.012E-3 for dynamic load), MAPE (0.036E-1 and 0.253E-1), MAE (0.776E-3 and 0.806E-3), and the highest R2-score (0.9998 and 0.9983), alongside zero relative error for both datasets. These results demonstrate the model's ability to capture the intricate degradation dynamics more effectively than existing state-of-the-art methods. By filling the technology gap of accurately modeling PEMFC degradation under varying load conditions, this study establishes a new standard for PEMFC prognostics and paves the way for future innovations in optimizing fuel cell performance and longevity.

Tailoring magnetism in chromium-doped zinc cobalt ferrite nanostructure for advanced spintronic memory devices

Soft magnetic ferrites serve as a crucial component in a wide array of sensing and actuating applications across various industries, including consumer electronics, automotive systems, spintronics, and more. These materials also find utility in spintronics, particularly for next-generation magnetic random-access memory (MRAM) due to their excellent inductive properties, which enhance the performance and scalability of spintronic memory devices. This study focuses on fine-tuning soft magnetic ferrites for inductive applications by doping them with Cr ions. By utilizing advanced experimental techniques such as the vibrating sample magnetometer (VSM) for bulk magnetometry and X-ray magnetic circular dichroism (XMCD) alongside X-ray absorption spectroscopy for atomic studies, the research aims to investigate the structural, electronic, and magnetic properties of the nanoferrites. Addition of doping in the soft ferrites observes reduction in the general magnetic parameters, with the only exception being coercivity, which decreases from 381 Oe to 275 Oe as the doping concentration increases from x = 0 to x = 0.02, and then increases to 350 Oe at x = 0.03. Through such precise tuning of the nanoparticles, this study aims to investigate the controllable soft magnetic properties of the nanoferrites, thereby paving the way for fast access times, non-volatility, and low energy consumption, presenting a compelling alternative to conventional memory technologies.

Curvilinear masks extend lithography options for advanced node memory roadmaps

Curvilinear masks extend lithography options for advanced node memory roadmaps

Electronic and Transport Engineering of A-Type Antiferromagnets with Ferroelectric Sandwich Structure: Toward Multistate Nonvolatile Memory Applications.

Achieving higher-order multistates with mutual interstate switching at the nanoscale is essential for high-density storage devices; yet, it remains a significant challenge. Here, we demonstrate that integrating A-type antiferromagnetic semiconductors sandwiched between ferroelectric layers is an effective strategy to achieve high-performance multistate data storage. Taking the Sc2CO2/VSi2P4 bilayer (bi-VSi2P4)/Sc2CO2 van der Waals multiferroic heterostructure as an example, our first-principles calculations show that by switching the polarization direction of the upper and bottom ferroelectric Sc2CO2 layers, antiferromagnetic bi-VSi2P4 can exhibit four distinct states with different band structures. The intriguing band structure engineering stems from the polarization-field-induced band shift and interface charge transfer. Accordingly, the proposed Sc2CO2/bi-VSi2P4/Sc2CO2-based multiferroic device can achieve four different resistance states, accompanied by fully spin-polarized currents and giant tunneling electroresistance ratios. Our results propose a viable strategy for realizing nonvolatile electrical control of antiferromagnets at the nanoscale and provide insights into the development of advanced memories.

Dynamic behavior and stability control of skyrmionium in periodic PMA/damping gradient nanowires

Magnetic skyrmioniums—with a composite structure comprising two skyrmions with opposite topological charges, exhibit unique dynamic behaviors that are crucial for technological advancements and have application potential for high-density and nonvolatile memory. This study explores the impact of periodic perpendicular magnetic anisotropy (PMA) and damping gradients on skyrmioniums. Utilizing the object oriented micromagnetic framework for detailed simulations, the effective control and enhancement of the skyrmionium stability and mobility through the periodic modulation of PMA and damping gradients is demonstrated. The results demonstrate the dynamic behavior and stability control of skyrmioniums in periodic PMA/damping gradient nanowires. Moreover, the critical influence of the periodic gradient on the skyrmionium motion and stability is highlighted. The results present new avenues for developing advanced memory technologies, leveraging skyrmionium's unique nonlinear behaviors to improve the device performance and reliability.

Ultralow-Energy-Consumption Photosynaptic Transistor Utilizing Conjugated Polymers/Perovskite Quantum Dots Nanocomposites With Ligand Density Optimization.

The photosynaptic transistor stands as a promising contender for overcoming the von Neumann bottleneck in the realm of photo-communication. In this context, photonic synaptic transistors is developed through a straightforward solution process, employing an organic semiconducting polymer with pendant-naphthalene-containing side chains (PDPPNA) in combination with ligand-density-engineered CsPbBr3 perovskite quantum dots (PQDs). This fabrication approach allows the devices to emulate fundamental synaptic behaviors, encompassing excitatory postsynaptic current, paired-pulse facilitation, the transition from short-to-long-term memory, and the concept of "learning experience." Notably, the phototransistor, incorporating the blend of the PDPPNA and CsPbBr3 PQDs washed with ethyl acetate, achieved an exceptional memory ratio of 104. Simultaneously, the same device exhibited an impressive paired-pulse facilitation ratio of 223% at a moderate operating voltage of -4V and an extraordinarily low energy consumption of 0.215 aJ at an ultralow operating voltage of -0.1mV. Consequently, these low-voltage synaptic devices, constructed with a pendant side-chain engineering of organic semiconductors and a ligand density engineering of PQDs through a simple fabrication process, exhibit substantial potential for replicating the visual memory capabilities of the human brain.

(Invited) 3D Integration of 2D Devices for Neuromorphic Computing

In this presentation, I will delve into the exciting realm of monolithic 3D integration, where emerging 2D FETs take center stage, empowering advanced memory, and logic devices. Furthermore, I will also discuss our work on bio-inspired neuromorphic computing. Notably, our recent breakthroughs have culminated in the successful demonstration of wafer-scale 2-tier and 3-tier 3D integration, utilizing MoS2 and WSe2 FETs as the building blocks. These achievements have paved the way for multifunctional circuits that hold immense promise for the future of electronics. Delving deeper into our research, we have harnessed the potential of 2D materials to design solid-state devices with low power consumption mimicking auditory processing in barn owl, collision avoidance in locust, and probabilistic computing in dragonfly. By combining the power of 2D materials with bio-inspired principles, our work lays a solid foundation for the creation of highly compact and functionally diverse integrated circuits in the revolutionary third dimension. The implications of this technology are far-reaching and hold the potential to shape the future of electronics and computing.

Current-driven dynamics of antiferromagnetic skyrmions: from skyrmion Hall effects to hybrid inter-skyrmion scattering

Antiferromagnetic (AFM) skyrmions have emerged as a highly promising avenue in the realm of spintronics, particularly for the development of advanced racetrack memory devices. A distinguishing feature of AFM skyrmions is the cancellation of their net topological charge, leading to an anticipated absence of the skyrmion Hall effect (SkHE). Here, we unveil that the latter is finite under the influence of spin-transfer torque, depending on the direction of the injected current impinging on intrinsic AFM skyrmions emerging in Cr/Pd/Fe trilayer on Ir(111) surface. Hinging on first principles combined with atomistic spin dynamics simulations, we identify the origin of the SkHE, which is due to the ellipticity of the skyrmions, and we uncover that FM skyrmions in the underlying Fe layer act as effective traps for AFM skyrmions, confining them and affecting their velocity. These findings hold significant promise for spintronic applications, the design of multi-purpose skyrmion tracks while advancing our understanding of AFM–FM skyrmion interactions and hybrid soliton dynamics in heterostructures.

Exploration and optimization of novel replacement and prefetching strategies for inefficiencies of advanced MRAM-based hybrid cache systems

With the emergence of cutting-edge hardware systems such as cloud computing, edge computing, and on-chip neural network accelerators, how to design advanced memory strategies to substitute the traditional ones for maximizing the potential performance of non-volatile memory (NVM) under the existing hardware conditions, has become an urgent research issue for both academia and industrial communities. It is promising and innovative to improve computer systems in the layer of data exchanging with the emerging advanced semiconductor devices. In the paper, to address the inefficiencies of write-intensive, high power consumption, low hit rate and so on, which exist in hybrid magnetic random access memory cache systems, three novel cache replacement strategies and two cache prefetching strategies are put forward. The proposed triple novel replacement strategies, including historical frequency and time judgments, duplicate data-aware deletion, and dynamic relevance factors computing, can be utilized to compensate for the shortcomings of the traditional least recently used replacement strategy, respectively. In the two novel prefetching strategies, region distribution parameters and Listnet ranking network are imported into the caching process, respectively, to achieve optimized hitting performance. The simulation results demonstrate that the proposed replacement strategies can achieve up to 61.76%, 84.91%, 56.49%, and 53.21% optimization of write count, hit rate, dynamic power, and IPC compared to the conventional one. The proposed prefetching strategy can achieve up to 91.27%, 49.25% hit rate and IPC optimization. Meanwhile, the synthetic evaluation of the replacement and prefetching strategies are elaborated in the paper, including multi-core characteristics, information entropy, interplays and the performance constraints between replacement and prefetching mechanism, which would facilitate more credible ideas for future memory inefficiencies management and strategy design.

The long-lasting benefits of pre-kindergarten education on autistic children's working memory development.

Working memory is an important skill for school success, and it involves holding information in our memory while using it to solve complex problems. However, autistic children often have difficulties with working memory tasks. Also, kindergarteners on the autism spectrum tend to be less school-ready compared with their peers. In addition, children from disadvantaged backgrounds tend to struggle more with working memory and school readiness skills. All preschool-age children on the autism spectrum in the United States are entitled to pre-kindergarten (pre-K) education. However, it is unclear whether attending pre-K helps with children's working memory development in the long run. This study tested whether attending pre-K benefits children's working memory development in the long run. It also tested whether pre-K is especially helpful for autistic children's working memory development. It was found that children who attended pre-K outperformed their peers who did not attend pre-K during the first 2 years of elementary school. However, after first grade, such benefits diminished. Importantly, autistic children who attended pre-K did not demonstrate advanced working memory immediately in kindergarten, but they started to outperform their autistic peers who did not attend pre-K during first grade to third grade. This finding highlights the importance of pre-K education for autistic children in particular. It is also important for educators and parents to understand autistic children's unique learning paths that may be different from non-autistic children. This article discusses specific ways for educators to take full advantage of the long-lasting benefits of pre-K education in autistic children's working memory development.

Negative Photoconductivity Transistors for Visuomorphic Computing.

Visuomorphic computing aims to simulate and potentially surpass the human retina by mimicking biological visual perception with an artificial retina. Despite significant progress, challenges persist in perceiving complex interactive environments. Negative photoconductivity transistors (NPTs) mimic synaptic behavior by achieving adjustable positive photoconductivity (PPC) and negative photoconductivity (NPC), simulating "excitation" and "inhibition" akin to sensory cell signals. In complex interactive environments, NPTs are desired for visuomorphic computing that can achieve a better sense of information, lower power consumption, and reduce hardware complexity. In this review, it is started by introducing the development process of NPTs, while placing a strong emphasis on the device structures, working mechanisms, and key performance parameters. The common material systems employed in NPTs based on their functions are then summarized. Moreover, it is proceeded to summarize the noteworthy applications of NPTs in optoelectronic devices, including advanced multibit nonvolatile memory, optoelectronic logic gates, optical encryption, and visual perception. Finally, the challenges and prospects that lie ahead in the ongoing development of NPTs are addressed, offering valuable insights into their applications in optoelectronics and a comprehensive understanding of their significance.

Improving OpenDevin: Boosting code generation LLM through advanced memory management

OpenDevin, a code generation AI tool, has emerged as a powerful assistant for both technical and non-technical users, offering a practical approach to coding challenges. Unlike traditional code generators that merely output code, OpenDevin excels by executing code directly in a console, allowing for immediate testing and verification. This functionality not only streamlines the coding process but also enhances learning and troubleshooting, making it accessible to a broader audience. In this project, we address several key challenges to improve OpenDevins effectiveness, especially in handling multi-round conversations and contextually relevant code generation. Our team identified and tackled two main challenges faced by OpenDevin: variety of input, and multi-step conversations. Through incorporating a series of functions to parse, summarize, and organize LLM agents memory logs, we significantly improved OpenDevin agents capabilities among a variety of tasks. The integration of efficient memory management led to a notable increase in accuracyfrom 44.4% to 88.9% in multi-round conversations, highlighting the importance of effective memory management in AI-powered coding tools. This report details our methodology, the challenges we faced, and the solutions we implemented, showcasing OpenDevins potential to revolutionize the way users from various backgrounds engage with coding tasks.

Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method

Abstract. The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation.

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Global–Local Deep Fusion: Semantic Integration with Enhanced Transformer in Dual-Branch Networks for Ultra-High Resolution Image Segmentation

The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, A novel fusion mechanism termed global–local deep fusion (GL-Deep Fusion) is introduced, based on an enhanced transformer architecture that efficiently integrates global contextual information and local details. Specifically, we propose the global–local synthesis networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Identifying tiny overlapping items is a task where the model excels, demonstrating its particular effectiveness. To optimize GPU memory utilization, a dual-branch architecture was meticulously designed. This architecture proficiently leverages the features it extracts and seamlessly integrates them into the enhanced transformer framework of GL-Deep Fusion. Benchmarks on the DeepGlobe and Vaihingen datasets demonstrate the efficiency and accuracy of the proposed model. It significantly reduces GPU memory usage by 24.1% on the DeepGlobe dataset, enhancing segmentation accuracy by 0.8% over the baseline model. On the Vaihingen dataset, our model delivers a Mean F1 score of 90.2% and achieves a mIoU of 90.9%, highlighting its exceptional memory efficiency and segmentation precision.

SuperAging functional connectomics from resting-state functional MRI.

Understanding the relationship between functional connectivity (FC) of higher-order neurocognitive networks and age-related cognitive decline is a complex and evolving field of research. Decreases in FC have been associated with cognitive decline in persons with Alzheimer's disease and related dementias (ADRD). However, the contributions of FC have been less straightforward in typical cognitive aging. Some investigations suggest relatively robust FC within neurocognitive networks differentiates unusually successful cognitive aging from average aging, while others do not. Methodologic limitations in data processing and varying definitions of 'successful aging' may have contributed to the inconsistent results to date. The current study seeks to address previous limitations by optimized MRI methods to examine FC in the well-established SuperAging phenotype, defined by age and cognitive performance as individuals 80 and older with episodic memory performance equal to or better than 50-to-60-year-olds. Within- and between-network FC of large-scale neurocognitive networks were compared between 24 SuperAgers and 16 cognitively average older-aged control (OACs) with stable cognitive profiles using resting-state functional MRI (rs-fMRI) from a single visit. Group classification was determined based on measures of episodic memory, executive functioning, verbal fluency and picture naming. Inclusion criteria required stable cognitive status across two visits. First, we investigated the FC within and between seven resting-state networks from a common atlas parcellation. A separate index of network segregation was also compared between groups. Second, we investigated the FC between six subcomponents of the default mode network (DMN), the neurocognitive network commonly associated with memory performance and disrupted in persons with ADRD. For each analysis, FCs were compared across groups using two-sample independent t-tests and corrected for multiple comparisons. There were no significant between-group differences in demographic characteristics including age, sex and education. At the group-level, within-network FC, between-network FC, and segregation measurements of seven large-scale networks, including subcomponents of the DMN, were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Thus, FC within or between large-scale networks does not appear to be a primary driver of the exceptional memory performance observed in SuperAgers. These results have relevance for differentiating the role of FC changes associated with cognitive aging from those associated with ADRD.