Static Memory Research Articles

Investigating the relationships between motor skills, cognitive status, and area deprivation index in Arizona: a pilot study.

Previous studies highlight the negative impact of adverse socioeconomic conditions throughout life on motor skills and cognitive health. Factors such as cognitive activity, physical activity, lifestyle, and socioeconomic position significantly affect general health status and brain health. This pilot study investigates the relationships among the Area Deprivation Index (ADI)-a measure of neighborhood-level socioeconomic deprivation, brain structure (cortical volume and thickness), and cognitive status in adults in Arizona. Identifying measures sensitive to ADI could elucidate mechanisms driving cognitive decline. The study included 22 adults(mean age = 56.2 ± 15.2) in Arizona, residing in the area for over 10 years(mean = 42.7 ± 15.8). We assessed specific cognitive domains using the NeuroTrax™ cognitive screening test, which evaluates memory, executive function, visual-spatial processing, attention, information processing speed, and motor function. We also measured cortical thickness and volume in 10 cortical regions using FreeSurfer 7.2. Linear regression tests were conducted to examine the relationships between ADI metrics, cognitive status, and brain health measures. Results indicated a significant inverse relationship between ADI metrics and memory scores, explaining 25% of the variance. Both national and state ADI metrics negatively correlated with motor skills and global cognition (r's < -0.40, p's < 0.05). In contrast, ADI metrics generally positively correlated with motor-related volumetric and cortical thickness measures (r's > 0.40, p's < 0.05). The findings suggest that neighborhood-level social deprivation might influence memory and motor status, primarily through its impact on motor brain health.

Association of handgrip strength asymmetry and weakness with cognitive function: a nationally representative cohort study

ObjectiveTo describe the association of handgrip strength asymmetry and weakness with cognitive function among Chinese middle-aged and older adults. Study designWe used data from four waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study. Handgrip strength was measured at baseline. Handgrip strength asymmetry was defined on the basis of the ratio of handgrip strength of the non-dominant hand to that of the dominant hand (i.e. non-dominant/dominant): a ratio of <0.9 defined as dominant handgrip strength asymmetry and >1.1 as non-dominant handgrip strength asymmetry. Weakness was defined as a handgrip strength of <28 kg for males or <18 kg for females. Main outcome measuresCognitive function with its two core dimensions (episodic memory and mental status) at each wave was assessed and standardized. Results9333 participants (48.3 % female, age 58.2 ± 9.0 years) were included. Non-dominant but not dominant handgrip strength asymmetry was significantly associated with poorer cognitive function at baseline (β = −0.121, −0.092, and −0.132 for mental status, episodic memory, and global cognition, respectively). In longitudinal analyses over 2 years, dominant handgrip strength asymmetry significantly slowed cognitive decline (β = −0.078 and −0.069 for mental status and global cognition, respectively), and non-dominant handgrip strength asymmetry accelerated cognitive decline (β = 0.053 and 0.043 for episodic memory and global cognition, respectively). Weakness was associated with poorer cognitive function at baseline and cognitive decline over 2, 4, and 7 years (all P < 0.05). ConclusionsIn middle-aged and older adults, non-dominant handgrip strength asymmetry and weakness were associated with poorer cognitive function and predicted accelerated cognitive decline. Dominant handgrip strength asymmetry may be beneficial for maintaining cognitive function.

Theta-phase locking of single neurons during human spatial memory.

The precise timing of single-neuron activity in relation to local field potentials may support various cognitive functions. Extensive research in rodents, along with some evidence in humans, suggests that single-neuron activity at specific phases of theta oscillations plays a crucial role in memory processes. Our fundamental understanding of such theta-phase locking in humans and its dependency on basic electrophysiological properties of the local field potential is still limited, however. Here, using single-neuron recordings in epilepsy patients performing a spatial memory task, we thus aimed at improving our understanding of factors modulating theta-phase locking in the human brain. Combining a generalized-phase approach for frequency-adaptive theta-phase estimation with time-resolved spectral parameterization, our results show that theta-phase locking is a strong and prevalent phenomenon across human medial temporal lobe regions, both during spatial memory encoding and retrieval. Neuronal theta-phase locking increased during periods of elevated theta power, when clear theta oscillations were present, and when aperiodic activity exhibited steeper slopes. Theta-phase locking was similarly strong during successful and unsuccessful memory, and most neurons activated at similar theta phases between encoding and retrieval. Some neurons changed their preferred theta phases between encoding and retrieval, in line with the idea that different memory processes are separated within the theta cycle. Together, these results help disentangle how different properties of local field potentials and memory states influence theta-phase locking of human single neurons. This contributes to a better understanding of how interactions between single neurons and local field potentials may support human spatial memory.

Differentiation between viral and autoimmune limbic encephalitis: a prospective cohort study with development and validation of a diagnostic model.

Distinguishing between viral encephalitis (VE) and autoimmune limbic encephalitis (ALE) presents a clinical challenge due to the overlap in symptoms. We aimed to develop and validate a diagnostic prediction model to differentiate VE and ALE. A prospective observational multicentre cohort study, which continuously enrolled patients diagnosed with either ALE or VE from October 2011 to April 2023. The demographic data, clinical features, and laboratory test results were collected and subjected to logistic regression analyses. The model was displayed as a web-based nomogram and then modified into a scored prediction tool. Model performance was assessed in both derivation and external validation cohorts. A total of 2423 individuals were recruited, and 1001 (496 VE, 505 ALE) patients were included. Based on the derivation cohort (389 VE, 388 ALE), the model was developed with eight variables including age at onset, acuity, fever, headache, nausea/vomiting, psychiatric or memory complaints, status epilepticus, and CSF white blood cell count. The model showed good discrimination and calibration in both derivation (AUC 0.890; 0.868-0.913) and external validation (107 VE, 117 ALE, AUC 0.872; 0.827-0.917) cohorts. The scored prediction tool had a total point that ranged from - 4 to 10 also showing good discrimination and calibration in both derivation (AUC 0.885, 0.863-0.908) and external validation (AUC 0.868, 0.823-0.913) cohorts. The prediction model provides a reliable and user-friendly tool for differentiating between the VE and ALE, which would benefit early diagnosis and appropriate treatment and alleviate economic burdens on both patients and society.

Attractors coexistence and representation research of Lotka–Volterra recurrent neural networks by matrix analysis theory

Attractor neural network models have been extensively studied in maintaining brain working memory state, spatial representation, and other brain processing mechanisms. The coexistence of multiple attractors in nonlinear neural network is particularly worth studying. The attractor is a stable state in the dynamic system, and all adjacent states will eventually be pulled to the attractor over time. Attractors can be regarded as the expression of data in low-dimensional state space, and different kinds of attractors can be used to process different information. The coexistence of multiple attractors means that a neural network can store multiple patterns, that is to say different memory mechanisms appear at the same time. Investigating the coexistence and competition of attractors within Lotka–Volterra(LV) recurrent neural networks presents a pivotal avenue for understanding the dynamical underpinnings of complex systems, particularly in the realm of computational neuroscience and ecological modeling. This paper studies the coexistence of different types of attractors in asymmetric Lotka–Volterra recurrent neural networks. Through the weight matrix block and matrix decomposition theory, we obtain the explicit expressions of continuous attractors and discrete attractors. Since neurons have multiple grouping methods, each weight matrix block satisfying certain conditions corresponds to different attractors. Different from traditional symmetric networks, the eigenvalues of the asymmetric weight matrices may be complex, which may correspond to ring attractors. Our research shows that attractors can coexist under certain conditions, depending on the weight matrix and external input. Moreover, through simulation experiments, we show that external input will cause the competition of attractors. Finally, we provide several simulations of attractor coexistence to illustrate our theory.

Animating memories: revealing the organisation of remembering in interaction at museum exhibits

In this article we will explore how people’s memories of past experiences and events are evoked through their interaction with, and around museum objects. By examining naturally-occurring interactions which emerge at exhibits, we investigate how people draw upon material objects within the museum- texts and exhibits, when vocalising their rememberings of past experiences and events, and address the role of the body in animating these occasioned memories. Thus, we can observe how memories are brought to life at exhibits- these past encounters supplying a means through which to engage in the present, and address how people’s aesthetic encounters are shaped by these vocal and bodily animated rememberings. We also examine a case in which a participant’s remembering is questioned, exploring how participants resolve the contested status of memory to arrive at a concerted way of remembering and experiencing an exhibit. The analysis contributes to discussions within the sociology of memory and memory studies, as well as to museology and related areas of research. It is based on field observations and video-recordings of interaction in museums and other exhibitions in Great Britain and Europe, adopting ethnomethodology and conversation analysis as its analytic attitude.

Analysing the shape memory behaviour of GnP-enhanced nanocomposites: a comparative study between experimental and finite element analysis

Shape memory polymers (SMPs) are capable of enduring significant deformations and returning to their original form upon activation by certain external stimuli. However, their restricted mechanical and thermal capabilities have limited their broader application in engineering fields. To address this, the integration of graphene nanoplatelets (GnPs) with SMPs has proven effective in enhancing their mechanical and thermal properties while maintaining inherent shape memory functions. The study evaluated shape memory nanocomposites (SMNCs) using dynamic mechanical, thermogravimetric, and static tensile, flexural, and shape memory tests, along with scanning electron microscopy to analyse tensile fractures. The results indicate that the optimal content of GnP is 0.6 wt%, resulting in excellent shape memory, thermal, and mechanical properties. Specifically, this composition demonstrates a shape recovery ratio of 94.02%, a storage modulus of 4580.07 MPa, a tensile strength of 61.42 MPa, and a flexural strength of 116.37 MPa. Additionally, the incorporation of GnPs into epoxy reduces recovery times by up to 52% at the 0.6 wt% concentration. While there is a slight decrease in the shape fixity ratio from 98.77% to 93.02%, the shape recoverability remains consistently high across all samples. Current finite element (FE) models often necessitate complex, problem-specific user subroutines, which can impede the straightforward application of research findings in real-world settings. To address this, the current study introduces an innovative finite element simulation method using the widely used ABAQUS software to model the thermomechanical behaviour of SMNCs, importantly incorporating the time-dependent viscoelastic behaviour of the material. The effectiveness of this new approach was tested by comparing experimental results from bending test of SMNCs cantilever beam with outcomes derived from FE simulations. The strong agreement between the experimental data and simulation results confirmed the precision and reliability of this novel technique.

Spatial evolution of the proton-coupled Mott transition in correlated oxides for neuromorphic computing.

The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO3-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis.

Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing

AbstractThis study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing.

Programmable Optoelectronic Synaptic Transistors Based on MoS2/Ta2NiS5 Heterojunction for Energy-Efficient Neuromorphic Optical Operation.

The optoelectronic synaptic transistors with various functions, broad spectral perception, and low power consumption are an urgent need for the development of advanced optical neural network systems. However, it remains a great challenge to realize the functional diversification of the systems on a single device. 2D van der Waals (vdW) materials can combine unique properties by stacking with each other to form heterojunctions, which may provide a strategy for solving this problem. Herein, an all-2D vdW heterojunction-based programmable optoelectronic synaptic transistor based on MoS2/Ta2NiS5 heterojunctions is demonstrated. The device implements reconfigurable, multilevel non-volatile memory (NVM) states through sequential modulation of multiple optical and electrical stimuli to achieve broadband (532-808nm), energy-efficient (17.2 fJ), hetero-synaptic functionality in a bionic manner. The intrinsic working mechanisms of the photogating effect caused by band alignment and the interfacial trapping defect modulation induced by gate voltage are revealed by Kelvin-probe force microscopy (KPFM) measurements and carrier transport analysis. Overall, the (opto)electronic synaptic weight controllability for combined in-sensor and in-memory logic processors is realized by the heterojunction properties. The proposed findings facilitate the technical realization of generic all 2D hetero-synapses for future artificial vision systems, opto-logical systems, and Internet of Things (IoT) entities.

Tuneable quantised conductance memory states in TiO2 based resistive switching devices in crossbar geometry for high density memory applications

The tunability and controllability of conductance quantization mediated multilevel resistive switching (RS) memory devices, fabricated in crossbar geometry can be a promising alternative for boosting storage density. Here, we report fabrication of Cu/TiO2/Pt based RS devices in 8 × 8 crossbar geometry, which showed reliable bipolar RS operations. The crossbar devices showed excellent spatial and temporal variability, time retention and low switching voltage (<1 V) and current (∼100 μA). Furthermore, during the reset switching, highly repeatable and reliable integral and half-integral quantized conductance (QC) was observed. The observed QC phenomenon was attributed to the two dimensional confinement of electrons as lateral width of the conducting filament (CF) matches the fermi wavelength. The magnitude and number of the QC steps were found to increase from ∼2.5 to 12.5 and from 5 to 18, respectively by increasing the compliance current (I C) from 50 to 800 μA which also increased the diameter of the CF from ∼1.2 to 3.3 nm. The enhancement in both number and magnitude of QC states was explained using electrochemical dissolution mechanism of CF of varying diameter. A thicker CF, formed at higher I C, undergoes a gradual rupture during reset process yielding a greater number of QC steps compared to a thinner CF. The realisation of QC states in the crossbar Cu/TiO2/Pt device as well as I C mediated tunability of their magnitude and number may find applications in high-density resistive memory storage devices and neuromorphic computing.

STUDY OF FLOOD VULNERABILITY IN HIMALAYAN REGION: A CASE OF UTTRAKHAND

India's Himalayan area is well known for its magnificent scenery and abundant wildlife. However, because of its complicated geology, delicate ecosystems, and unpredictable climate, it is also extremely vulnerable to catastrophic calamities. The causes, effects, and mitigation techniques of the common natural catastrophes that affect India's Himalayan region—such as earthquakes, landslides, floods, and glacial lake outburst floods (GLOFs)—are briefly summarised in this summary. Because of the collision of the Indian and Eurasian tectonic plates, earthquakes are one of the most dangerous natural disasters in the Himalayan region. Major seismic events, like the one that struck Nepal in 2015, demonstrate how vulnerable the region's heavily inhabited areas are to devastating destruction and high fatality rates. Effective disaster preparedness and resilient infrastructure are crucial for minimizing casualties and damage. Another common risk in the Himalayan region is landslides, which can be brought on by unstable slopes, strong rains, and seismic activity. The region's susceptibility is exacerbated by these occurrences, which represent a major threat to natural ecosystems, transportation networks, and towns, particularly during the monsoon season. In the Himalayan region, floods are a common occurrence that are exacerbated by glacial melt, heavy rainfall, and glacier lake outbursts. Flash floods, like the ones that occurred in Uttarakhand in 2013, highlight the terrible effects on infrastructure, communities, and the environment. Reducing flood risks and increasing resilience requires community-based adaptation strategies, early warning systems, and sustainable land use planning. A unique hazard in the Himalayan region, glacial lake outburst floods (GLOFs) are caused by the rapid melting of glaciers and the creation of unstable glacial lakes. The communities, infrastructure, and ecosystems downstream are seriously threatened by these catastrophes, which calls for careful observation and risk assessment techniques. One of the worst natural catastrophes to have hit the state in recent memory is the flash floods in Uttarakhand. Because to the state's distinctive geography, which is made up of small valleys and steep hills, heavy rainfall events have a greater effect, especially during the monsoon season. 2013's devastating flash floods, brought on by abnormally high rainfall, caused extensive environmental damage, infrastructure ruin, and a great deal of fatalities. The abrupt influx of water, rubble, and silt caused great destruction to isolated communities, tourist attractions, and places of worship, underscoring the precariousness of human habitation in the foothills of the Himalayas. The incident also brought attention to the relationship between unplanned development, environmental deterioration, and the likelihood of disasters, spurring calls for more land use planning, early warning systems, and sustainable development techniques future flash floods in Uttarakhand. In conclusion, the broad range of natural hazards that the Himalayan area of India faces calls for multifaceted methods to disaster risk reduction and management. In this dynamic and vulnerable setting, scientific research, community participation, and sustainable development practices are critical components of effective resilience-building efforts. Keywords: Hilly areas natural disaster, flash flood, Glacial lake outburst, flash flood in Uttarakhand

Emergence of Ferroelectricity in p‐Type 2D In1.75Sb0.25Se3

p‐type 2D ferroelectric semiconductors (2D FeSs) play an increasingly essential role in the advanced nonvolatile and morphotropic beyond‐Moore electronic devices with high performance and low power consumption. But reliable p‐type 2D FeS with holes as majority carriers are still scarce. Herein, the first experimental realization of room‐temperature ferroelectricity in van der Waals layered β‐In1.75Sb0.25Se3 down to few layer is reported. The origin of ferroelectricity in β‐In1.75Sb0.25Se3 comes from aliovalent elemental substitution, antimony substituting to the indium sites (SbIn), changing the local environment of the central‐layer Se atoms. Thanks to the intrinsic ferroelectric and semiconducting natures, FeS field‐effect transistor (FeSFET) devices based on β‐In1.75Sb0.25Se3 exhibit reconfigurable, multilevel nonvolatile memory (NVM) states, which can be successively modulated by gate voltage stimuli. Furthermore, the inherent operation mechanism, due to the switchable polarization, indicates that a neuromorphic memory is also possible with 2D FeSFETs. These presented results facilitate the technological implementation of versatile 2D FeS devices for next‐generation logic‐in‐memory approach for Internet‐of‐Things entities.

Impact of channel thickness on device scaling in vertical InGaZnO channel charge-trap memory transistors with ALD Al2O3 tunneling layer

This study investigates the impact of channel thickness (TCH) variation on memory performance and its physical origins in vertical channel charge trap memory (V-CTM) using InGaZnO (IGZO) channels for X–Y scaling. When the TCH decreased, the subthreshold slope (SS) increased from 0.21 to 0.30 V/dec, while the minimum program voltage decreased from 12 to 8 V, indicating a performance trade-off. The analysis of bulk trap density extracted by SS, density of states distribution, and x-ray photoelectron spectroscopy confirmed an increase in the number of trap states and hydrogen-related defects in the IGZO channel when a thin TCH is employed. As a result, it was expected that the barrier height at the active layer/TL would be lowered during program operations at relatively thin channel. However, the SS would also be degraded due to an increase in sub-gap states, such as OH- and VOH, induced by hydrogen incorporation. The V-CTM with an optimal TCH of 7 nm had a maximum memory window of 5.7 V. The program/erase states were maintained for 106 s before experiencing a 50 % charge loss, and 5-level memory states were verified to be available within a memory window of 3.2 V for 108 s with little degradation. It was noteworthy that the long-term reliability and multilevel implementation could be secured for the V-CTM with a TCH as thin as 7 nm, demonstrating the sound feasibility of its X–Y scaling. This work presents new key parameters and understanding of device integration issues for future 3D structured memory transistors, and aims to contribute to the advancement of memory technologies using oxide semiconductor channels.

Don Mariano Marcos Memorial State University Medical OutPatient Records Management System: A Prototype

Exploring a transformative solution is driven by the inefficiencies inherent in the traditional manual, paper-based outpatient record management system at Don Mariano Marcos Memorial State University (DMMMSU). This research introduces the Medical Out-Patient Records Management System (MORMS) to fill this gap. Developed through the software development lifecycle, this OMRMS optimally maintains outpatient records; therefore, it assists medical professionals in recording, organizing, and retrieving patient data effortlessly. Notably, statistical reports represent data trends that are an invaluable product. The overall approval of MORMS was unanimous during User Acceptance Testing (UAT), with each tested component receiving a 100 percent acceptability index. Based on this assessment, it can be concluded that there is a high likelihood of integrating the system into the daily processes of DMMMSU’s medical officers across all its three campuses because its usefulness is valued by them. Moreover, usability testing validates the functional practicality of MORMS with respect to the Computer System Usability Questionnaire subscales -system usefulness, information quality, and interface quality. Consistent median ratings of 1 for these scales indicate pragmaticity or practicability; dependability; precision and ease of use, which are some merits of this methodical tool. Consequently, not only gaining acceptance from users but also being highly usable makes MORMS effective in complying with various requirements set by different groups within DMMMSU’s medical staff.

The association between memory, COVID-19 testing, and COVID-19 incidence in middle-aged and older adults: a prospective analysis of the CLSA

ABSTRACT We investigated the association between pre-COVID-19 memory function and (a) receipt of a COVID-19 test and (b) incidence of COVID-19 using the COVID-19 Questionnaire Study (CQS) of the Canadian Longitudinal Study on Aging (CLSA). The CQS included 28,565 middle-aged and older adults. We regressed receipt of a COVID-19 test on participants’ immediate and delayed recall memory scores and re-ran the regression models with COVID-19 incidence as the outcome. All regression models were adjusted for sociodemographic, lifestyle, and health covariates. In the analytical sample (n = 21,930), higher delayed recall memory (better memory) was significantly associated with lower COVID-19 incidence. However, this association was not significant for immediate recall memory. Immediate and delayed recall memory were not associated with receipt of a COVID-19 test. Health policymakers and practitioners may viewmemory status as a potential risk for COVID-19. Memory status may not be a barrier to COVID-19 testing.

A new [formula omitted] control method of singular time-delay systems based on a variable memory state feedback

This paper is concerned with the H∞ control problem of singular time-delay systems. A variable memory state feedback is established with state decomposition. It is verified that this feedback including the traditional memory and memoryless state feedbacks as special cases, and thus it is more general than these existing feedback strategies Based on the proposed feedback, a new H∞ control method with better performance level is derived. Moreover, the yielded computational complexity is lower. The superiority is confirmed by two given examples.

Far-gate synaptic transistors utilizing ion-charge dual-transfer mechanism for neurotransmitter-multiplexing temporal coding

The ability of artificial synapses to replicate multiplexed-transmission is a significant advancement in emulating complex brain activities. However, it generally required more stringent material requirements of intrinsic-ambipolarity and more complex structures of P/N dual-channel. Here, we proposed a far-gate synaptic transistor (FGST) just using a single-channel composed of a common unipolar semiconductor to emulate the cooperation and competition between two excitatory neurotransmitters. FGST exhibits a unique ion-charge dual-transfer mechanism, enabling distinct behavioral regulation modes with switchable synaptic plasticity: ion-dominant potentiation-depression with short-term plasticity and hole-dominant potentiation with enhanced memory. Moreover, FGST with dual-excitatory enhancement can be used for temporal contrast encoding, dividing currents into multiple memory states based on a fixed threshold; by comparing the variations in postsynaptic currents with different thresholds, it offers a method for further expanding the number of temporal states in the device. This work is a step toward constructing multifunctional artificial intelligent systems.

Acute stress impairs intentional memory suppression through aberrant prefrontal cortex activation in high trait ruminators

ObjectiveResearch shows that the effect of acute stress on intentional memory suppression could be modulated by individual differences in psychological traits. However, whether acute stress distinctly affects intentional memory suppression in high trait ruminators, a high at-risk group of stress-related disorders, and the neural correlations, remains unclear. Method55 healthy college students were divided into high and low trait ruminators (HTR and LTR), Following stress manipulation, a Think/No Think task assessed the memory suppression performance. Functional near-infrared spectroscopy was applied to explore the neural correlates. Psychophysiological interaction analyses were used to assess how the functional connectivity between a seed region and another brain region was modulated by tasks during memory suppression, further mediating memory suppression performance and state rumination. ResultsThe HTR exhibited poorer memory suppression performance than the LTR under the stress condition. Aberrant activation patterns and task-modulated functional connectivity in the dorsal prefrontal cortex (DLPFC) and superior temporal gyrus (STG) were observed only in the HTR during memory suppression under the stress condition. The effect of memory suppression performance on the state rumination of individuals was significantly mediated by the task-modulated functional connectivity between the DLPFC and STG. ConclusionsThe findings could provide insights for prevention or early intervention in the development of stress-related disorders in HTR.