Memory Characteristics Research Articles

Nogo-A-mediated constraints on activity-dependent synaptic plasticity and associativity in rat hippocampal CA2 synapses.

Hippocampal area CA2 has garnered attention in recent times owing to its significant involvement in social memory and distinctive plasticity characteristics. Research has revealed that the CA2 region demonstrates a remarkable resistance to plasticity, particularly in the Schaffer Collateral (SC)-CA2 pathway. In this study we investigated the role of Nogo-A, a well-known axon growth inhibitor and more recently discovered plasticity regulator, in modulating plasticity within the CA2 region. The findings demonstrate that blocking Nogo-A in male rat hippocampal slices facilitates the establishment of both short-term and long-term plasticity in the SC-CA2 pathway, while having no impact on the Entorhinal Cortical (EC)-CA2 pathway. Additionally, the study reveals that inhibiting Nogo-A enables association between the SC and EC pathways. Mechanistically, we confirm that Nogo-A operates through its well-known co-receptor, p75 neurotrophin receptor (p75NTR), and its downstream signaling factor such as Rho-associated protein kinase (ROCK), as their inhibition also allows plasticity induction in the SC-CA2 pathway. Additionally, the induction of long-term depression (LTD) in both the EC and SC-CA2 pathways led to persistent LTD, which was not affected by Nogo-A inhibition. Our study demonstrates the involvement of Nogo-A mediated signaling mechanisms in limiting synaptic plasticity within the CA2 region.

Tunable sub-threshold current firing via insulator-to-metal transition enabled by lithographic nanochannels for neuromorphic applications

The electric field-driven insulator-to-metal transition (IMT) offers a promising platform for developing controllable, futuristic neuromorphic nanoelectronics. However, the volatile nature of IMT, typically stimulated by a specific threshold voltage, limits its potential use primarily to switch-like applications. To broaden its applications, including in-material data processing, achieving on-demand IMT activation with dynamic memory capability is essential. This study demonstrates on-demand modulation of IMT behavior using spatially confined VO2 nanochannels, designed by local probe lithography. This approach enables the integration of ultrafast (∼180 ns) volatile switches (on/off ratio >103) and memory storage, from short- to long-term, in a single device. Notably, the threshold voltage was effectively reduced from 5.6 V to 2.8 V by precisely modulating the width of spatially embedded VO2 nanochannels. The observed memory behavior is attributed to persistent metallic domains and preferential IMT along these channels, as confirmed by optical and Kelvin probe force microscopy. Furthermore, the ability to classify input patterns, even in the presence of noise, was demonstrated using interconnected coplanar nanochannels by leveraging the short-term memory characteristics of the IMT. This report marks a significant step towards on-demand nanoscale manipulation of the IMT dynamics, laying the groundwork for ultrasmall, high-speed, and energy-efficient conventional and neuromorphic nanoelectronics.

The moderating role of information processing speed in the relationship between brain remodeling and episodic memory in amnestic mild cognitive impairment.

The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated. Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single-region, connectivity, and network levels. IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex. IPS functioned as a moderator of associations between recall and neuroimaging metrics at the "single region-connectivity-network" level, providing new insights for cognitive rehabilitation in aMCI patients. aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.

A review of memristive reservoir computing for temporal data processing and sensing

AbstractReservoir computing (RC) is a promising paradigm for machine learning that uses a fixed, randomly generated network, known as the reservoir, to process input data. A memristor with fading memory and nonlinearity characteristics was adopted as a physical reservoir to implement the hardware RC system. This article reviews the device requirements for effective memristive reservoir implementation and methods for obtaining higher‐dimensional reservoirs for improving RC system performance. In addition, recent in‐sensor RC system studies, which use a memristor that the resistance is changed by an optical signal to realize an energy‐efficient machine vision, are discussed. Finally, the limitations that the memristive and in‐sensor RC systems encounter when attempting to improve performance further are discussed, and future directions that may overcome these challenges are suggested.

Alginate-based artificial antigen presenting cells expand functional CD8+ T cells with memory characteristics for adoptive cell therapy

The development of artificial Antigen Presenting Cells (aAPCs) has led to improvements in adoptive T cell therapy (ACT), an immunotherapy, for cancer treatment. aAPCs help to streamline the consistent production and expansion of T cells, thus reducing the time and costs associated with ACT. However, several issues still exist with ACT, such as insufficient T cell potency, which diminishes the translational potential for ACT. While aAPCs have been used primarily to increase production efficiency of T cells for ACT, the intrinsic properties of a biomaterial-based aAPC may affect T cell phenotype and function. In CD8+ T cells, reactive oxygen species (ROS) and oxidative stress accumulation can activate Forkhead box protein O1 (FOXO1) to transcribe antioxidants which reduce ROS and improve memory formation. Alginate, a biocompatible and antioxidant rich biomaterial, is promising for incorporation into an aAPC formulation to modulate T cell phenotype. To investigate its utility, a novel alginate-based aAPC platform was developed that preferentially expanded CD8+ T cells with memory related features. Alginate-based aAPCs allowed for greater control of CD8+ T cell qualities, including, significantly improved in vivo persistence and augmented in vivo anti-tumor T cell responses.

Thermal-driven self-healing and recyclable thermosetting polyurethane resins for energy harvesting

Improving the mechanical properties, self-healing capabilities, and recyclability of friction layer materials is crucial for the advancement of high-performance triboelectric nanogenerators (TENG). In this study, self-healing cross-linked polyurethanes were synthesized through the prepolymer technique, employing the Diels-Alder (D-A) reaction between the furan ring and Bismaleimide (BMI). The tensile strength of PU film with 10 wt% MBI is 54 MPa, and the shape recovery rate is 96.99 %. Moreover, it exhibits excellent self-healing and recyclable characteristics. Surface cracks can be healing within 10 min under heat, reshaped through hot pressing, while retaining 93.3 % of its mechanical properties. Additionally, the 2 cm × 2 cm area PU-based TENG can generate a short-circuit current, open-circuit voltage, transferred charge, and power density of 3.6 μA, 89 V, 29 nC, and 2.5 W/m2, respectively, at a frequency of 1 Hz. Futhermore, the self-healing efficiency of mechanical properties and the triboelectric output performances were found to reach 93.3 % and 98 %, respectively, after reprocessing. This study introduces a promising method for enhancing self-healing, shape memory characteristics, and the design of wearable TENG devices.

Carbon dots as a distinctive platform fabricated through a sustainable approach for versatile applications

Red-emitting carbon dots (R-CDs) have garnered significant interest owing to its exceptional fluorescence (FL) characteristics. In this study, we present a one-pot solvothermal synthesis for producing biomass-derived R-CDs, utilizing Birch leaves as the primary raw material. The R-CDs display high solubility in ethanol and demonstrate deep-red emission at 625 nm with narrowband characteristics. After conducting a selectivity assessment with different metal ions, it is observed that R-CDs demonstrate exceptional sensitivity and specificity specifically to Fe3+. This demonstrates a linear relationship up to a concentration of 500 μM, with a detection limit of 0.302 μM (S/N=3). In this research, we created photoluminescent and transparent composites made from polyvinyl alcohol (PVA) with embedded fluorescent R-CDs, using the solution casting method for fabrication. The synthesized R-CDs exhibit robust and consistent FL in solution, whereas, the R-CDs@PVA nanocomposites (NCs) films are transparent and exceptionally flexible. The optimized 0.08 wt% R-CDs demonstrated the most effective ultra violet (UV) blockage across the UV-A (76.84 %), UV-B (74.86 %), and UV-C (78.46 %) region. However, PVA alone absorbed < 25 % of the light in all UV regions. The study explored the shape memory and FL characteristics of the R-CDs@PVA NCs changed under varying conditions such as water, temperature and pH. In alkaline conditions, the FL intensity of the R-CDs@PVA NCs increased due to the deprotonation of the R-CDs surface. In addition, R-CDs dispersed within PVA exhibit superior ink-free patterned substrates, proving to be advantageous for non-destructive template relief printing. Furthermore, R-CDs serve as effective anti-oxidants, combating 2,2-Diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and mitigating the oxidative stress (OS) in red blood cells (RBCs) induced by sodium nitrite (NaNO2). They aid in the restoration of essential stress indicators, encompassing lipid peroxidation (LPO), protein carbonyl content (PCC), total thiol (TT), superoxide dismutase (SOD), and catalase (CAT). Furthermore, R-CDs restored the damaged liver, kidney, heart, and pancreas by regulating biochemical parameters. Furthermore, incorporated R-CDs into red light-emitting diodes (R-LEDs) and white light-emitting diodes (W-LEDs), thereby enhancing plant growth efficiency. The results of this study highlight the possible of R-CDs as non-toxic, UV blocking agents suitable for developing edible coatings and packaging. These properties suggest their applicability in the food industry, anti-counterfeiting (AC) measures, and biomedical applications.

Ultratough Supramolecular Polyurethane Featuring an Interwoven Network with Recyclability, Ideal Self-Healing and Editable Shape Memory Properties.

Developing multifunctional polymers with excellent mechanical properties, outstanding shape memory characteristics, and good self-healing properties is a formidable challenge. Inspired by the woven cross-linking strategy, a series of supramolecular polyurethane (PU) with an interwoven network structure composed of covalent and supramolecular cross-linking nodes have been successfully synthesized by introducing the ureido-pyrimidinone (UPy) motifs into the PU skeleton. The best-performing sample exhibited ultrahigh strength (∼77.2 MPa) and toughness (∼312.7 MJ m-3), along with an ideal self-healing efficiency (up to 90.8% for 6 h) and satisfactory temperature-responsive shape memory effect (shape recovery rates up to 96.9%). Furthermore, it ensured recyclability. These favorable properties are mainly ascribed to the effective dissipation of strain energy due to the disassembly and reconfiguration of supramolecular nodes (i.e., quadruple hydrogen bonds (H-bonds) between UPy units), as well as the covalent cross-linking nodes that maintain the integrity of the polymer network structure. Thus, our work provides a universal strategy that breaks through the traditional contradictions and paves the way for the commercialization of high-performance multifunctional PU elastomers.

A novel temperature drift compensation method based on LSTM for NMR sensor

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor.

A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance.

Optimal routing and end-to-end entanglement distribution in quantum networks

Quantum networks are designed to transmit quantum bits (qubits) among quantum devices to enable new network resources for the applications. Entanglement distribution and entanglement swapping are fundamental procedures that are required in several network operations. However, they are probabilistic operations, which can lead to severe network performance degradation. This article investigates the engineering problem of resource allocation in quantum networks, considering factors like entanglement distribution probability, quantum memory characteristics, and fidelity. We model this as an optimization model to obtain an optimal solution. In particular, we formulate an integer linear programming (ILP) and develop a heuristic algorithm, aiming to minimize the number of required entangled qubit pairs (Bell pairs or EPR pairs) in any adjacent pair in the quantum network. Extensive simulations are performed to compare the performance of proposed ILP and heuristic. In all the cases, the heuristic produces a comparable solution to the optimal one. Simulation results ensure that the value of maximum utilized Bell pairs in a quantum network highly depends on the value of the probability of entangled pairs established, considering the time in the quantum memory besides the number of incoming requests.

Executive functions and addictions: a Structural Equation Modeling Approach and the Italian validation of the Adult Executive Functioning Inventory (ADEXI)

Introduction the work aims to observe the associations between psychoactive substance use and gambling and executive functioning as well as to validate the Italian version of the “Adult Executive Functioning Inventory” (ADEXI) scale. Methods data were collected through a representative cross-sectional study among 5,160 people (18-84 years old) called IPSAD® (Italian Population Survey on Alcohol and Other Drugs). Structural Equation Modeling (SEM) was performed to explore the associations between ADEXI and other behaviors measured with standardized questionnaires. Cronbach α has been performed to investigate the psychometric properties of the Italian version of the ADEXI scale. Results SEM showed that both WM and INH were correlated with problematic cannabis use (WM r = 0.112; INH r = 0.251) and gambling (WM r = 0.101; INH r = 0.168), while problematic alcohol use was correlated only with INH (r = 0.233). Cronbach α for the WM subscale was 0.833 (CI 0.826-0.840), while for INH was 0.694 (CI 0.680-0.708). Conclusion results pointed out a strong correlation between addictions (substance-related and non-substance-related) and WM and INH impairments among the adult general population. Moreover, the ADEXI scale could be considered a valuable tool for general population surveys to detect working memory and inhibition characteristics.

Physical Reservoir Computing Using van der Waals Ferroelectrics for Acoustic Keyword Spotting.

Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP2S6 (CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.

Potential for multi-application advancements from doping zirconium (Zr) for improved optical, electrical, and resistive memory properties of zinc oxide (ZnO) thin films

Transition metal-doped Zinc oxide (ZnO) thin films with an optimal wide band gap and semiconducting nature find numerous applications in optoelectronic devices, gas sensors, spintronic devices, and electronics. In this study, Zirconium (Zr) doped ZnO thin films were deposited on ITO (Indium Tin oxide) coated glass substrate using RF-magnetron sputtering. Optical and electrical properties were examined for their potential use in resistive random-access memory (RRAM) applications. X-ray Diffraction (XRD), UV–vis spectroscopy, x-ray photoelectron spectroscopy (XPS), Atomic force microscopy (AFM) and Scanning electron microscopy (SEM) were used to investigate structural, optical, and compositional properties and roughness respectively. The results demonstrate that the films possess crystalline properties. Additionally, an augmentation in Zr concentration correlates with an elevation in the optical band gap, ascending from 3.226 eV to 3.26 eV, accompanied by an increase in Urbach energy from 0.0826 eV to 0.1234 eV. The film with the highest Zr content among all the films demonstrated the best electrical performance for resistive memory applications. Incorporating Zr as a dopant shows enhancement in the electrical performance and such ZnO films with optimum concertation of Zr can potentially be used in RRAM. ZnO being a versatile host material, its doping with Zr may extend its applications in catalysis, gas sensing, energy storage, and biomedical engineering. ZnO thin films employ zirconium (Zr) as a dopant, which is a novel way to improve the material’s characteristics. Although ZnO has been thoroughly researched, adding Zr presents a novel technique to enhance optical, electrical, and resistive memory characteristics all at once that has not been fully investigated.

A fully floating memristor emulator with long-term memory

In this paper, we proposed a fully floating memristor emulator with long-term memory characteristics. The circuit comprises operational amplifiers and an analog switch. Switching between incremental and decremental modes is easily achieved by changing the polarity of the input signal. The key feature of the emulator is its long-term memory, made possible by the switch and voltage follower. The correctness of the derived formula is verified using Matlab, and the emulator's pinched hysteresis loop and non-volatility are assessed using LTspice. Additionally, an experimental platform was constructed for physical testing, with the physical results showing consistency with the simulations. Finally, the proposed emulator was applied to a simple read-write circuit, demonstrating its practical applicability.

Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

Applying (ARFIMA) Model for Forecast the Saudi Stock Market Prices

The interest in the topic of time series forecasting has increased during the recent years and thus appeared specific modern methods, for example Autoregressive Fractional Integrated Moving Average model (ARFIMA), or what is called long memory model, which use fractionally difference (d) instead of integer which used in ARIMA models. In this study we displayed long memory feature and its tests. Our discussion supported by analysing the real time series (daily closing index of the Saudi Arabia Stock Market prices) over period 1/1/2018 to 19/12/2022, including 1240 observations, to make a good model to giving forecast results for future, using statistical tests and statistical software (R- 4.2.2 program). Firstly we checked that the data series was unstable by testing unit root, using Dickey fuller and Philips Perron tests ; and confirmed the presence of a long memory pattern in it by calculating the Hurst exponent (H), then calculated the fractional differential coefficient (d), determined the appropriate models for analysis and prediction, then the best model was chosen among them based on the comparison criteria, then predicted the values for the next 5 days, R - 4.2.2 software was used in all of these tests and predictions Statistical results indicated that the optimal model to represent the data series is ARFIMA (3,0.383,2) which used to predict future values.

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

High-Dimensional Physical Reservoir with Back-End-of-Line-Compatible Tin Monoxide Thin-Film Transistor.

This work demonstrates a physical reservoir using a back-end-of-line compatible thin-film transistor (TFT) with tin monoxide (SnO) as the channel material for neuromorphic computing. The electron trapping and time-dependent detrapping at the channel interface induce the SnO·TFT to exhibit fading memory and nonlinearity characteristics, the critical assets for physical reservoir computing. The three-terminal configuration of the TFT allows the generation of higher-dimensional reservoir states by simultaneously adjusting the bias conditions of the gate and drain terminals, surpassing the performances of typical two-terminal-based reservoirs such as memristors. The high-dimensional SnO TFT reservoir performs exceptionally in two benchmark tests, achieving a 94.1% accuracy in Modified National Institute of Standards and Technology handwritten number recognition and a normalized root-mean-square error of 0.089 in Mackey-Glass time-series prediction. Furthermore, it is suitable for vertical integration because its fabrication temperature is <250 °C, providing the benefit of achieving a high integration density.

High‐performance shape memory characteristics by integrating urea linkages into the polyurethane elastomer

AbstractShape memory polymers have been widely researched to develop multifunctional materials that respond smartly to external stimulations and programmed signals. Among them, shape memory polyurethane (SMPU) is drawing great attention owing to its highly tunable properties and ease of integrating new functions. However, the effect of urea bonds on SMPU elastomers has rarely been investigated because of the extreme reactivity of amine crosslinkers. In this study, we used diethanolamine (DEOA), which contains a secondary amine, as the crosslinker for SMPU synthesis. Unlike other crosslinkers containing primary amines, they can form urea bonds in a more gentle manner. The urea bonds reinforce the intermolecular interactions between the hard segments with strong hydrogen bonding, thus increasing the phase separation and degree of crystallization. Consequently, the urea‐containing SMPU exhibits improved mechanical strength and shape memory abilities for both fixing and recovery. Moreover, the transcarbamoylation reaction, which enables the reconfiguration of the original shape, is observed. The introduction of the urea bonds into the SMPU elastomer can be beneficial in achieving flawless shape memory performances in various sensitive applications.