Search Results (2,966)

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

Background/Objectives: Neuritogenesis and synaptogenesis support learning and cognitive function, and hippocampal neurons play central roles in these processes. Cleistocalyx nervosum var. paniala (CNP), a Southeast Asian berry, has reported neuroprotective activities, but its direct effects on hippocampal neurons remain unclear. We investigated whether CNP extract modulates hippocampal neuronal transcriptomes, neuritogenesis, and synaptogenesis. Methods: Primary hippocampal neurons isolated from male and female Wistar rat pups were treated with CNP extract in vitro. Cytotoxicity was assessed to define non-cytotoxic concentrations. Transcriptomic responses were profiled by RNA sequencing and validated by RT-qPCR. Neuritogenesis was quantified by neurite morphology and Sholl analysis. Synaptogenesis was evaluated by synaptic immunocytochemistry. Molecular docking of cyanidin-3-glucoside (C3G) and resveratrol was used to generate mechanistic hypotheses. Results: At 0.1–10 µg/mL, CNP was non-cytotoxic, whereas a 100 µg/mL dose reduced viability; therefore, 10 µg/mL was used in subsequent experiments. Exploratory RNA-seq profiling identified thousands of differentially expressed genes enriched in synapse- and neurite-related pathways, including synaptogenesis signaling, axon guidance, and neuritogenesis. RT-qPCR showed upregulation of Igf1 in males and Glul in females, with sex-dependent modulation of Bdnf and Cask. CNP increased neurite length, branching, and Sholl complexity in both sexes, with a more pronounced effect in males. A male-biased effect was also observed in synapse-related marker colocalization, with increased Syn1–Psd95 colocalization detected in males. Docking suggested plausible interactions of C3G and resveratrol with regulators such as MYC, TP53, and CREB1. Conclusions: CNP extract alters transcriptional networks and enhances neurite outgrowth in primary hippocampal neurons in a sex-dependent manner, with male-biased effects on Syn1–Psd95 colocalization. These findings support further dose–response, mechanistic, and sex-stratified in vivo studies to evaluate its neurobiological potential. Full article

Background: Polygenic risk scores for Alzheimer’s disease (AD), organized by gene networks shared between the blood and brain, may provide insights into underlying disease mechanisms common to both tissues. Methods: We derived a blood–brain network-based polygenic risk score (nbPRS) from AD-associated genetic variants for three blood-brain networks, selected by the preservation of blood and brain gene co-expression networks, and AD association. Participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 1109), Framingham Heart Study (FHS, n = 8310), the Religious Orders Study Memory Aging Project (ROSMAP, n = 1215), and Mount Sinai Brain Bank (MSBB, n = 323) were stratified into low- and high-nbPRS subgroups, then profiled using longitudinal and cross-sectional data. We compared the conversion from normal cognition to AD between nbPRS subgroups. Genes differentially expressed among low- and high-nbPRS individuals were profiled with classical neuropathological markers and we investigated potential biologically relevant pathways for the genes significantly expressed in high-risk individuals. Results: Individuals with high nbPRS in three AD-associated networks (M2, M6, M14) demonstrated significant impairment in executive function and memory performance, whereas high-risk individuals in networks M2 and M14 had significantly reduced hippocampal volume. We observed high-risk individuals in M2 and M14 developed AD at twice the rate of low-risk individuals in these networks. HLA genes were differentially expressed with transcriptome-wide significance among low- and high-nbPRS individuals in M14 and associated with neuroinflammatory and tau pathology. Conclusions: Polygenic risk scores derived from blood and brain networks can differentiate individuals with a high risk of AD conversion. Full article

Brain-Derived Neurotrophic Factor (BDNF), a key member of the neurotrophin family, is critically involved in neuronal survival, synaptic plasticity, learning, and memory. While its roles in mammals have been extensively documented, the molecular regulatory mechanisms governing BDNF expression and its causal contributions to complex cognitive behaviors remain poorly understood in non-mammalian vertebrates—particularly for the domestic pigeon (Columba livia domestica), a species distinguished by its remarkable spatial navigation and homing capabilities. This review synthesizes the current evidence on BDNF in the pigeon central nervous system across five thematic domains: molecular structure and isoform diversity, transcriptional and epigenetic regulatory networks, involvement in neural development, associations with cognitive and navigational behaviors, and potential translational applications. A particular emphasis is placed on the region-specific and activity-dependent expression patterns of BDNF in brain structures such as the hippocampal formation (HF), optic tectum, and striatum, and their functional relevance to visual processing, homing behavior, and stress adaptation. To date, most findings remain correlational; therefore, establishing a mechanistic understanding necessitates the integration of advanced methodologies—including single-cell omics, CRISPR-based gene editing, and high-resolution behavioral phenotyping—to causally link BDNF dynamics, neural circuit modulation, and spatial cognition. This synthesis aims to bridge gaps in comparative neurobiology, inform molecular approaches to avian cognitive enhancement, and support evidence-based strategies for racing pigeon breeding and welfare assessment. Full article

This study investigates how social networking service (SNS) tourism information characteristics influence destination image and behavioral intentions in digital tourism communication. Drawing on the stimulus–organism–response (S-O-R) framework, SNS information characteristics are conceptualized as vividness, convenience, interactivity, and reliability, and their effects on affective image, cognitive image, and SNS behavioral intentions are examined. Data were collected from 273 Chinese tourists who used SNS platforms to obtain information about Jeju Island, and structural equation modeling (SEM) with bootstrapping was employed to test direct and mediating effects. Results indicate that convenience significantly influences cognitive image; vividness, convenience, and interactivity significantly affect affective image; and reliability shows no significant effect. Affective image positively influences behavioral intentions, whereas cognitive image does not. In addition, vividness, interactivity, and reliability directly influence behavioral intentions, while convenience has no direct effect. Mediation analysis shows that affective image partially mediates the effects of vividness and interactivity and fully mediates the effect of convenience, whereas cognitive image does not exhibit a significant mediating role. These findings highlight the importance of affective mechanisms in digital tourism communication and provide practical implications for sustainable destination marketing and digital tourism management. Full article

Accurate modeling of learners’ evolving cognitive states is essential for intelligent educational systems, yet many existing knowledge tracing and graph-based approaches rely on static structures or purely sequential representations that inadequately capture dynamic structural changes in learning processes. This study proposes a Learner Cognitive Graph (LCG) framework that integrates dynamic heterogeneous graph modeling, structured behavioral data acquisition, and reinforcement learning-based intervention optimization. A Dynamic Cognition Graph (DCG) is formally defined as a sequence of temporally evolving graph snapshots representing interactions among learners, knowledge concepts, and exercises. A reverse Turing test-based agent with structured prompting is introduced to collect reasoning-oriented behavioral evidence, improving data reliability for cognitive modeling. Temporal message passing, multi-scale memory updating, and self-supervised learning objectives are employed to construct dynamic cognitive representations. Personalized intervention is formulated as a Markov decision process to optimize long-term learning outcomes. Experiments conducted on real-world and simulated educational datasets demonstrate improved knowledge mastery prediction accuracy, cognitive state transition modeling, and intervention efficiency compared with representative baselines. The proposed framework provides a systematic and scalable approach for dynamic cognitive modeling and adaptive educational support. Full article

This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain–computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications. Full article

Recent research demonstrates that guilt, as a self-conscious moral emotion, can shape early visual perception. However, the underlying neural mechanisms remain unclear. Using a pre–post experimental design combined with electroencephalography (EEG), we investigated how guilt modulates visual size perception and its neurophysiological correlates. Across four experiments, we confirmed that guilt emotion consistently increased the size overestimation component of the Ebbinghaus illusion. Time–frequency analyses revealed that guilt processing involved decreased prefrontal theta (4 to 7 Hz) power and reduced phase coupling of prefrontal theta and temporo-parieto-occipital alpha (8 to 12 Hz) oscillations. The guilt-related modulation of visual size perception was specifically associated with occipital alpha phase coherence. These results demonstrate that guilt emotion shapes fundamental visual processing through coordinated neural oscillations across large-scale brain networks. The findings advance understanding of emotion–cognition interactions and have implications for guilt-related psychiatric disorders. Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Traditional cognitive diagnosis models typically rely on expert-annotated Q-matrices to define the relationship between exercises and knowledge concepts. This process is not only highly subjective and costly, but also prone to introducing noise and bias, which directly affects diagnostic accuracy. Meanwhile, most existing deep learning-based methods overlook the rich semantic information contained in concept descriptions, making it difficult to deeply model the intrinsic relationships among knowledge points, resulting in limited interpretability of the models. To address these issues, this paper proposes a cognitive diagnosis model that incorporates key textual information from concept descriptions to refine the Q-matrix (KECQCD). The core innovation of the model lies in leveraging the pre-trained language model RoBERTa to encode concept texts, fusing semantic features with identifier embeddings through a gating mechanism to construct semantically-enhanced concept representations. It designs a concept-exercise heterogeneous information network and employs a graph attention mechanism to adaptively aggregate node features, explicitly modeling high-order knowledge dependencies. Furthermore, a multi-task joint learning framework is established to predict student performance while dynamically correcting association errors in the initial Q-matrix. Experimental results on the public Junyi dataset show that the KECQCD model significantly outperforms mainstream baseline models across multiple metrics, including accuracy (ACC), area under the curve (AUC), and root mean square error (RMSE). Ablation studies confirm the effectiveness of each core module, and diagnostic consistency (DOA) evaluation further demonstrates the enhanced interpretability of the model’s outcomes. This research offers a new solution for building accurate, reliable, and interpretable cognitive diagnosis systems, contributing positively to the advancement of personalized intelligent education. Full article

There are still challenges in speech emotion recognition, as the representation capability of single-modal information is limited, there are difficulties in capturing continuous emotional transitions in discrete emotion annotations, and the issues of modal structural differences and cross-sample alignment in multimodal fusion methods persist. To address these, this study undertakes work from both data and model perspectives. For data, a Chinese multimodal database STEM-E²VA was constructed, synchronously collecting four modalities of data: articulatory kinematics, acoustics, glottal signals, and videos. This covers seven discrete emotion categories and employs PAD continuous annotation. By integrating discrete and continuous dimensional annotations, it better represents the distinction between strong and weak emotions under the same discrete emotion label. Concurrently, to process the biases in PAD annotations, we employed the SCL-90 psychological questionnaire to analyze annotators’ cognitive and emotional perceptions, thereby ensuring data reliability. For model, this paper proposes a multimodal supervised contrastive fusion network incorporating PAD perception. It employs a PAD-enhanced hybrid contrastive loss function to optimize intra-model and inter-modal feature alignment. Utilizing a cross-attention mechanism combined with a GRU–Transformer network for temporal feature extraction, it achieves deep fusion of multimodal information, reducing inter-modal discrepancies and cross-class confusion. Experiments demonstrate that the proposed method achieves 85.47% accuracy in discrete sentiment recognition on STEM-E²VA, with a substantial reduction in RMSE for PAD dimension prediction. It also exhibits excellent generalization capability on IEMOCAP, providing a novel framework for integrating discrete and continuous sentiment representations. Full article

Cognitive or visual distraction caused by drivers using mobile phones, operating the central console, or conversing with passengers while driving is a significant contributing factor to road traffic accidents. Aiming to solve the problem that existing driving behavior monitoring systems exhibit insufficient recognition accuracy and low real-time detection performance in complex driving environments, this study proposes a dual-flow driver distraction detection model based on Sobel edge detection (DFSED-Model). The model is designed with a collaborative learning framework: the first flow adopts a lightweight pre-trained backbone network to achieve efficient semantic feature extraction. The second flow utilizes Sobel edge detection to extract the driver’s driving contours and enhances the model’s spatial sensitivity to driving movements and hand movements. Through the feature learning process of the first-flow-guided auxiliary branch, collaborative optimization of knowledge transfer and attention focusing is realized, thereby improving the model’s convergence speed and discriminative performance. The proposed model is evaluated on three widely used public datasets: the State Farm Distracted Driver Detection (SFD) dataset, the 100-Driver dataset, and the American University in Cairo Distracted Driver Dataset (AUCDD-V1). Under the premise of maintaining low computational overhead, the accuracy of the DFSED-Model reaches 99.87%, 99.86%, and 95.71%, respectively, which is significantly superior to that of many mainstream models. The results demonstrate that the proposed method achieves a favorable balance between accuracy, parameter count, and efficiency, and possesses strong practical value and deployment potential. Full article

Cognitive impairment persists in people with HIV (PWH) despite effective combination antiretroviral therapy, possibly as a result of persistent alterations in white matter microstructural abnormalities in the brain. Noninvasive tensor-valued diffusion MRI (dMRI) is sensitive to microstructural integrity; thus, it may contribute to the understanding of HIV-associated cognitive impairment. In this exploratory cross-sectional study, 31 healthy controls (HCs) and 24 PWH underwent 3T MRI and neurocognitive assessment. Tensor-valued dMRI metrics, including microscopic fractional anisotropy (µFA) and isotropic, anisotropic, and total mean kurtosis (MKi, MKa, MKt), and conventional DTI and DKI metrics (FA, MD, and MK) were evaluated across six functionally defined brain networks. Compared with HCs, PWH exhibited reduced FA, µFA, and MKa in the dorsal default mode and anterior salience networks, along with increased MKi in the salience network and decreased MKi in the executive control network, with moderate effect sizes. Compared with HCs, PWH performed significantly worse on measures of learning, memory, and language, but showed no differences in executive function, attention, or processing speed. Additionally, significant associations and interactions between dMRI metrics and HIV status were observed, particularly for MKi and attention, executive function, and processing speed across the default mode, salience, and executive control networks. These preliminary findings underscore tensor-valued dMRI as a sensitive biomarker of network-specific neurocognitive vulnerability in HIV. Full article

Bilingualism has increasingly been understood as a multidimensional and context-sensitive experience, prompting growing interest in how specific aspects of bilingual language use relate to cognition. We used Bayesian psychometric network analysis to examine how bilingual language practices, bicultural identity management, and cognition relate within the same system in a sample of 404 U.S.-born heritage Spanish–English bilingual adults of Latin American descent. This approach conceptualizes bilingualism as a complex system, quantifies uncertainty in the estimated network structure, and identifies aspects of bilingual experience that serve as bridges to cognition and bicultural identity. The strongest bridges between domains were the edge between language mixing and attentional control and the edge between unintended language switching and bicultural harmony. These findings provide a more holistic and socially infused characterization of how bilingualism, biculturalism, and cognition interact in U.S. heritage speakers of Spanish. Full article