Cogn Neurodyn [JOURNAL]
Sun
200 papers
Origin of non-zero overlap in Hopfield neural networks beyond storage capacity.
Fang F, Wang SJ, Huang ZG
Cogn Neurodyn · 2026 Dec · PMID 41647147 · Full text
Hopfield networks are widely used models of associative memory. When the number of stored patterns exceeds the network's storage capacity, theoretical predictions show that the overlap between final states and memorized...
Hopfield networks are widely used models of associative memory. When the number of stored patterns exceeds the network's storage capacity, theoretical predictions show that the overlap between final states and memorized patterns should vanish. However, numerical simulations show that a small, non-zero overlap persists, indicating that the network retains residual memory. To investigate the origin of this phenomenon, we analyze the network's dynamics during the initial update steps. Using a signal-to-noise-ratio analysis, we demonstrate that when a node undergoes a state flip, the signal term of its neighbors is enhanced by the connecting link. This effect improves the stability of these neighboring neurons, facilitating a fraction of the network to remain aligned with the memory pattern and preventing a total loss of memory. Our findings elucidate the mechanism by which residual memory traces emerge in Hopfield networks beyond the storage limit.
M3T-attention: a multi-level multi-scale temporal attention transformer for EEG hand movement trajectory decoding.
Zhu L, Jiang P, Huang A … +2 more , Zhang J, Yuan P
Cogn Neurodyn · 2026 Dec · PMID 41647146 · Full text
In recent years, brain-computer interface (BCI) technology has made significant progress in the fields of neural engineering and human-computer interaction. Among these advances, decoding upper-limb movements from electr...
In recent years, brain-computer interface (BCI) technology has made significant progress in the fields of neural engineering and human-computer interaction. Among these advances, decoding upper-limb movements from electroencephalography (EEG) signals has become a key research focus. However, most existing studies concentrate on discrete classification tasks (e.g., motor imagery recognition), while the prediction of three-dimensional continuous movement trajectories still faces several major challenges. These include the low signal-to-noise ratio of EEG signals, substantial inter-subject variability that limits generalizability, and the high degrees of freedom in 3D trajectories, which increase decoding complexity. To address these challenges and improve the accuracy of decoding continuous 3D hand movement trajectories from EEG signals, this study proposes a Multi-level Multi-scale Temporal Attention Transformer framework (M3T-Attention). The model is designed to extract temporal features across multiple time scales from EEG signals and integrate them via cross-scale attention mechanisms, enabling a nonlinear mapping from 0.5 to 12 Hz EEG signals to 3D kinematic parameters (position, velocity, and acceleration). The model was trained using EEG and wrist kinematic data from the WAY-EEG-GAL dataset. Experimental results show that the proposed method achieves Pearson correlation coefficients (PCCs) of 0.8816, 0.8841, and 0.8711 on the X, Y, and Z axes, respectively, demonstrating robust prediction performance across all subjects and outperforming existing state-of-the-art approaches. In summary, through comparative experiments, statistical significance analysis, and ablation studies, we have fully verified its ability to capture neural coding patterns. It significantly enhances the decoding performance from EEG signals to movement trajectories, offering new approaches for BCI applications in complex motor control scenarios. We have made the model's source code publicly available on GitHub repository URL: https://github.com/jjspp/M3T_Attention.
EEG microstate dynamics are consistently associated with depressive symptoms in healthy young adults.
Tarailis P, Griškova-Bulanova I
Cogn Neurodyn · 2026 Dec · PMID 41647145 · Full text
Early detection of depressive symptoms is crucial for reducing their impact on social and cognitive functioning and can be effectively supported by non-invasive, cost-effective biomarkers derived from brain electrical ac...
Early detection of depressive symptoms is crucial for reducing their impact on social and cognitive functioning and can be effectively supported by non-invasive, cost-effective biomarkers derived from brain electrical activity. Previous research has identified altered temporal and transition patterns of EEG microstates in clinical populations diagnosed with major depressive disorder (MDD) as well as in healthy individuals exhibiting elevated depressive symptoms. In this study, we aimed to replicate recent EEG microstate findings in young, generally healthy adults who reported high ( = 38) versus low ( = 38) levels of depressive symptoms, while also examining the long-range dependencies of microstate sequences. Microstate analysis was performed on 5-minute resting-state EEG recordings obtained with eyes closed. EEG data were categorized into five microstate classes, revealing significant differences in parameters between groups. Participants with high depressive symptoms exhibited decreased occurrence of microstate A, reduced coverage of microstates A and D, and diminished bidirectional transition probabilities between microstates A and D. Conversely, increased values were found for the Hurst exponent and bidirectional transition probabilities between microstates B and C, between microstates C and E, and from microstate B to E. Linear regression analysis demonstrated that these microstate parameters can predicted depressive symptom scores (R² = 0.145). Our results underscore the potential of resting-state EEG microstate temporal and sequence parameters as biomarkers for the early identification of depressive symptoms in generally healthy young adults.
Design of intelligent neuro-supervised deep learning networks to analyze brain electrical activity rhythms of Parkinson's disease model.
Saqib SU, Fang SH, Raja MAZ … +2 more , Nisar KS, Shoaib M
Cogn Neurodyn · 2026 Dec · PMID 41647144 · Full text
Parkinson's disease (PD) is a multidimensional neurological condition designated by dopamine-sensitive neuron decline, which impairs generator and cognitive function. To study the dynamics of Parkinson's disease (PD), th...
Parkinson's disease (PD) is a multidimensional neurological condition designated by dopamine-sensitive neuron decline, which impairs generator and cognitive function. To study the dynamics of Parkinson's disease (PD), this paper presents a novel methodology that uses Intelligent Systems Neuro-Supervised Deep Learning Networks (INSDLNs). To describe the patterns of electrical activity in the brain metrics throughout various points in the central nervous system, we suggest a model based on mathematics governed by three distinct classes. To gain a deeper understanding of the fundamental processes underlying Parkinson's disease development, we aim to identify obscure trends within neurological data by leveraging intelligent neuro-supervised learning networks. This novel approach may lead to improved diagnostic and therapeutic approaches and holds promise for improving our understanding of the dynamics of Parkinson's disease (PD). By utilizing the features of an architecture containing multilayer recurrent layers, the suggested Intelligent Systems Neuro-Supervised Deep Learning Networks (INSDLNs) are designed. The input and target samples for INSDLNs were organizedand constructed from reference data that was formulated using the Adams method on a range of PI scenarios for modeling using a reliable numerical solver. To evaluate the impact on patterns of brain electrical activity, this method involved moving sensor positions.The differential equations are used for creating the dataset using Mathematica's ND solve function. The dataset for INSDLNs training was generated using the Adam stochastic solver. After that, this dataset is divided into three significant states: 80% is used for training, 10% is used for validation, and 15% is used for testing. The goal of these divisions is to effectively handle the difficulties presented by the dynamical model. The datasets, randomly divided into training, testing, and validation samples, were used to apply the INSDLNs created for the study. To ensure the model's stability and efficacy on various data sets, the procedure for segmentation was executed by optimizing a fitness function based on mean squared error. The proposed INSDLNs demonstrate accuracy, preciseness, and security through the achievement of minimal mean squared error (MSE), complete regression analysis (Rg. As), optimized error histograms (Err. Hg), auto-correlation of error (AC of Err), cross-correlation of input with error (CCIEr), and minimal absolute error (Ab. Er).When modeling the brain rhythms of Parkinson's disease, our INSDLNs outperformed LMBPA and BRM with very low error (MSE: 5.86E-12 ± 2.1E-12), nearly zero absolute error, and strong regression accuracy (R2 ≈ 0.998).A lower mean square error (MSE) shows that the suggested approach operates effectively and that the forecasts generated by the model are more reliable. Reaching an almost zero absolute error (Ab. Er) provides more evidence for INSDLNs. These results highlight the higher accuracy and predictive power attained by applying INSDLNs and pursuing the best possible solutions.
Saccadic eye movements based classification of patients with obsessive-compulsive disorder, patients with schizophrenia and healthy controls using artificial neural networks.
Koliaraki MN, Smyrnis N, Asvestas P … +2 more , Matsopoulos GK, Ventouras EC
Cogn Neurodyn · 2026 Dec · PMID 41647143 · Full text
Intrusive saccades during active visual fixation indicate deficits in inhibitory control which is crucial for cognitive control function. Research has shown that abnormalities in these mechanisms are linked to neurologic...
Intrusive saccades during active visual fixation indicate deficits in inhibitory control which is crucial for cognitive control function. Research has shown that abnormalities in these mechanisms are linked to neurological disorders such as schizophrenia and obsessive-compulsive disorder (OCD), both involving dysfunctions in frontal-subcortical circuits. Eye movement studies and machine learning (ML) techniques have been used to differentiate clinical from neurotypical populations. This study aimed to classify healthy controls, patients with OCD and schizophrenia patients, based on oculomotor behavior during active fixation tasks and provide insights into related neurophysiological mechanisms. Data from three visual fixation tasks were analyzed using statistical tests to select saccade features to be used in the classification. A shallow Artificial Neural Network (ANN) was implemented for binary and three-class classification. Binary classification achieved 87% accuracy and 93% specificity in distinguishing controls from the patients with schizophrenia group, 84% accuracy and 90% sensitivity in distinguishing between controls and medicated patients with OCD not taking antipsychotics, while differentiation between patients with schizophrenia and medicated patients with OCD not taking antipsychotics reached 77% accuracy and 82% specificity. The findings provided indications that selected saccadic features can differentiate OCD and schizophrenia patients from healthy controls using shallow ANNs, while distinguishing between OCD and schizophrenia patients remains more challenging. Notably, tentative indications were provided that group differences were driven more by intrinsic saccadic generation properties than by fixation or inhibitory mechanisms, concerning unwanted saccades that are intrusive in nature in the context of fixation.
Five-class motor imagery BCI classification and its application to brain-controlled wheelchairs.
Pan H, Teng B, Liu Z … +3 more , Tong S, Yu X, Li Z
Cogn Neurodyn · 2026 Dec · PMID 41647142 · Full text
Brain-controlled wheelchair (BCW) technology enables direct wheelchair control via brain-computer interfaces (BCIs), eliminating the need for physical limb interaction. Motor imagery-based BCIs (MI-BCIs) are widely used...
Brain-controlled wheelchair (BCW) technology enables direct wheelchair control via brain-computer interfaces (BCIs), eliminating the need for physical limb interaction. Motor imagery-based BCIs (MI-BCIs) are widely used in non-invasive BCIs due to their ability to provide intuitive neural control without external stimuli. However, developing a BCW system based on MI-BCIs remains challenging, particularly in achieving reliable multi-class classification accuracy.To address this challenge, this study proposes an advanced feature extraction algorithm to enhance MI-BCI performance using a custom-built five-class MI-EEG dataset. The proposed method, EHT-CSP, integrates Ensemble Empirical Mode Decomposition Hilbert-Huang Transform (EEMD-HHT) with Time-Frequency Common Spatial Pattern (TFCSP). Specifically, it extracts marginal spectrum entropy and energy spectrum entropy via EEMD-HHT. It then combines these features with TFCSP-derived feature vectors to improve feature discrimination. The Light Gradient Boosting Machine is then employed for classification. The proposed MI-BCI system is evaluated through both offline analysis and real-world BCW obstacle avoidance experiments. Results demonstrate that the algorithm achieves an average classification accuracy of 78.45%, with all participants successfully completing BCW navigation tasks. In this study, LightGBM and EHT-CSP are compared with other algorithms respectively, and it is verified that the proposed model is superior to the existing models.
A novel dilated Bi-LSTM framework for depression detection from speech signals through feature fusion.
Jaishankar U, Nirmal JH, Gidaye G
Cogn Neurodyn · 2026 Dec · PMID 41647141 · Full text
UNLABELLED: A crucial method for determining a person's mental health and assessing their degree of depression is depression detection. To identify depression through speech or conversation, a number of sophisticated met...
UNLABELLED: A crucial method for determining a person's mental health and assessing their degree of depression is depression detection. To identify depression through speech or conversation, a number of sophisticated methods and questionnaires have been created. The constraints of the current system are as follows: reduced effectiveness as a result of poor feature selection and extraction, problems with interpretability, and the difficulty of identifying depression in different languages. As a result, the proposed model is presented to offer improved accuracy and efficient performance. While adaptive threshold-based pre-processing (AdaT) is used to eliminate quiet and unnecessary information, the twinned Savitzky-Golay filter (TSaG) is used to minimize noise in the dataset. To turn the signal into an image, a Synchro-Squeezed Adaptive Wavelet Transform Algorithm (SSawT) is employed. The Singular Empirical Decomposition and Sparse Autoencoder (SiFE) model is used to extract linear and deep features. Input's deep, linear, and statistical properties are combined using the Weighted Soft Attention-based Fusion (WSAttF) model. From the fused features, the Chaotic Mud Ring Optimization algorithm (ChMR) chooses the best features. A Dilated Convolutional Neural Network (CNN) based Bidirectional-Long Short Term Memory-Bi-LSTM (DiCBiL) is used to detect different stages of depression, which lowers error rates and increases detection accuracy. The proposed method achieves 93.22% of F1-score, 93.11% precision, 93.12% recall, and 93.31% accuracy on the DAIC-WOZ original test set. During the testing time, two more datasets, namely AVEC 2019 and MELD, are used to validate the proposed performance, attaining an accuracy of 93.91% and 85.34% respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10411-9.
Delay dynamics within the neuroglial electromagnetic coupling system.
Yuan Z, Song J, Feng P … +1 more , Zhang R
Cogn Neurodyn · 2026 Dec · PMID 41647140 · Full text
Building upon our prior introduction of the Delay concept within a neuron-astrocyte electromagnetic coupling system, this study provides a deeper investigation into this phenomenon. The focus is on a specific time interv...
Building upon our prior introduction of the Delay concept within a neuron-astrocyte electromagnetic coupling system, this study provides a deeper investigation into this phenomenon. The focus is on a specific time interval, termed Delay, which occurs after the cessation of external stimuli. During this period, neurons continue their firing activity before transitioning to a resting state. We initially elucidate that the prolonged neuronal firing, termed Delay, originates from astrocytic involvement rather than magnetic effects. Moreover, the periodic calcium activity of astrocytes can periodically induce the occurrence of neuronal Delay. Finally, we provide a thorough analysis of the duration and structural composition of the neuron Delay induced by astrocytes. The significance of our findings lies in the potential functional role of the Delay phase in the modulation and processing of neural information. Our findings offer a novel perspective on the complex dynamics governing the transition from active firing to resting in neurons, thereby enhancing the understanding of neural response and adaptability.
Automated differentiation of parkinsonian disorders: an ROI-based analysis of subcortical shape and cortical surface features.
Dehghan Y, Sarbaz Y
Cogn Neurodyn · 2026 Dec · PMID 41647139 · Full text
UNLABELLED: The clinical manifestations of early-stage parkinsonian syndromes overlap, making accurate differential diagnosis crucial yet challenging. This study aimed to develop a system for automated differentiation of...
UNLABELLED: The clinical manifestations of early-stage parkinsonian syndromes overlap, making accurate differential diagnosis crucial yet challenging. This study aimed to develop a system for automated differentiation of idiopathic Parkinson's disease (IPD) from progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). Our sample included clinical data and T1-weighted magnetic resonance imaging from 50 IPD, 47 PSP, and 38 CBS patients. We introduced an atlas-based approach to extract shape features from subcortical regions in each subject's native coordinate image space. The surface thickness and folding parameters were also extracted from cortical regions. A statistical analysis was conducted to identify regions with significant differences in the extracted features, followed by the employment of a feed-forward neural network to distinguish these patients. Significant structural differences were observed in several regions, including the thalamic nuclei, basal ganglia, midbrain, cerebellum, cingulate cortex, and insula. Using only cortical surface features, our diagnostic model outperformed the model that relied solely on subcortical shape features. However, the classifier achieved its best predictive performance when incorporating features from both cortical and subcortical structures, yielding an accuracy of 86.1% in a multi-class classification system and 96.1% for distinguishing IPD from PSP and CBS, as well as an accuracy of 94.2% for classifying CBS versus PSP in a two-class classification system. Our findings underscore the significance of cortical morphological patterns and demonstrate that the proposed methodology could potentially serve as an automated diagnostic system in clinical settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10402-2.
Kinetic parameters sensitive to cognitive activity during walking for diagnosis of Parkinson's disease.
Zhao H, Xie J, Wei G … +5 more , Liu A, Jones R, Qu Q, Cao H, Cao J
Cogn Neurodyn · 2026 Dec · PMID 41647138 · Full text
Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions. An objective and easily measurable digital marker is crucial for improving the diagnosis and monitoring of PD. Sin...
Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions. An objective and easily measurable digital marker is crucial for improving the diagnosis and monitoring of PD. Since gait is a complex activity that requires both motor control and cognitive input, this study assumes that kinetic parameters of the foot sensitive to the cognitive load (dual-tasking) for healthy adults can be used to diagnose PD. In this study, walking with a cognitive task has been conducted on healthy subjects, the kinetic parameters have been calculated with algorithms of inverse dynamics in Opensim. Subsequently, the moment-related variables, including the bend and force of the plantar surface, were collected from 13 patients with PD and 32 healthy controls using the wearable system. Statistical analysis of the focused kinetic parameters indicates that the moment of the metatarsophalangeal joint has a significant difference between dual-task walking and single walking. The experimental results demonstrate that features extracted from the bend and force signal of the plantar surface can diagnose PD with an average accuracy of 95.55% with 5-fold cross validation. It demonstrates that kinetic data from the foot captured by wearable sensors can serve as an objective digital marker for PD.
Trading time for space: a new approach to investigate the EEG neural mechanisms of fine motor brain based on ICA-optimized traceability network analysis.
Gong A, Man H, Shi X … +5 more , Li S, Hu X, Gong B, Shi T, Fu Y
Cogn Neurodyn · 2026 Dec · PMID 41647137 · Full text
UNLABELLED: Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limita...
UNLABELLED: Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limitation makes it challenging to accurately localize and characterize activity within specific target regions of the brain. To address this, we propose a computational model for brain-network analysis based on independent component analysis (ICA) and source-space clustering. First, repetitive ICA decomposition is performed on a trial-by-trial basis, followed by clustering to extract stable independent components and their corresponding spatial mapping vectors. Subsequently, standardized low-resolution brain electromagnetic tomography (sLORETA) is employed for source localization. The resulting source locations are then clustered across trials to define network nodes, which are utilized to construct a source-level brain network for the investigation of neural mechanisms. The efficacy of this algorithm was validated using two datasets: the international Brain-Computer Interface (BCI) competition dataset involving motor imagery, and a self-collected dataset recorded during the preparatory phase of pistol shooting. Analysis of the motor-imagery dataset demonstrated that the proposed method identified active brain regions consistent with those observed in previous functional magnetic resonance imaging (fMRI) studies. Regarding the pistol-shooting preparation dataset, the method revealed heightened activity in the frontal, occipital, and bilateral temporal lobes. Furthermore, the intensity of information interaction among multiple brain regions exhibited a significant correlation with shooting performance. These findings not only corroborate prior research but also uncover novel features regarding source-level functional connectivity. Consequently, this novel framework achieves precise source localization and network analysis using EEG, significantly enhancing spatial resolution and providing a more accurate elucidation of target brain activities and information-interaction mechanisms during motor tasks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10405-z.
State-dependent alterations of network characteristics between seizure and non-seizure onset zones in drug-resistant epilepsy.
Guo K, Meng K, Yu R … +5 more , Zhang L, Hu Y, Zhang R, Yao D, Chen M
Cogn Neurodyn · 2026 Dec · PMID 41647136 · Full text
UNLABELLED: Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-sei...
UNLABELLED: Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-seizure onset zones (NSOZ) across different seizure stages, the intracranial electroencephalogram (iEEG) data based brain networks from 29 DRE patients have been constructed using the weighted phase lag index (WPLI) and phase transfer entropy (PTE), respectively. Then, graph theory metrics, such as eigenvector centrality, betweenness centrality, in-degree and out-degree, are calculated to compare network characteristics of SOZ and NSOZ nodes across interictal, pre-ictal, early-ictal and post-ictal periods in multiple frequency bands. Statistical analyses demonstrate that the SOZ exhibits significantly higher eigenvector centrality and betweenness centrality in the beta and gamma frequency bands, serving as network hubs and primary sources of information outflow. By contrast, the NSOZ shows elevated centrality only in the theta and alpha frequency bands during non-ictal states. Moreover, during the pre-ictal to early-ictal transition, the SOZ progressively evolves into hub nodes with enhanced outflow and reduced inflow, whereas the NSOZ shifts toward non-hub status with increased inflow. Importantly, the random forest model utilizing out-degree features of early-ictal gamma frequency band can effectively identify the SOZ, and achieve an area under the curve (AUC) of 0.82. Overall, these findings offer a novel network-based perspective on the state-dependent alterations of epileptic seizures in DRE and contribute to the treatment of epilepsy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10400-4.
Identifying electrophysiological signatures of anticipatory and reactive processing in a discrimination response task in professional dancers.
Casella A, Gasparotti C, Panacci C … +7 more , Boccacci L, Filosa M, Aydin M, Ferrulli N, Sciaretta S, Di Bello B, Di Russo F
Cogn Neurodyn · 2026 Dec · PMID 41647135 · Full text
This study investigated the electrophysiological correlates of anticipatory and reactive processing and behavior associated with a visuomotor discrimination response task of professional dancers to test the effect of dan...
This study investigated the electrophysiological correlates of anticipatory and reactive processing and behavior associated with a visuomotor discrimination response task of professional dancers to test the effect of dance practice on their cognitive functions. To control the physical activity practice effects, dancers were compared with non-dancers matched for physical activity level. Considering the intrinsic features of the training routine to which professional dancers are constantly exposed - characterized by high temporal anticipation, continuous spatial monitoring and complex sensorimotor integration - we hypothesized differences in attentional control mechanisms and anticipatory processes compared to physically active controls in a discrimination response task. Behavioral data showed that dancers were more accurate than controls, and they had comparable response times. This effect was paralleled by the analysis of event-related potential (ERP), showing dancers compared to controls larger cognitive preparation in the prefrontal cortex (PFC), indexed by the prefrontal negativity (pN) ERP component. This may indicate a more intense top-down attentional control of the upcoming task. Dancers also showed reduced early sensory processing (P1 component) and less intense stimulus-response mapping (pP2 component), suggesting more efficient reactive processing in early sensory processing and associative brain areas. In contrast, the pP1 component was enhanced in dancers, likely reflecting superior sensory-motor integration, a pivotal function in choreographic demands. No difference emerged in the P3, signaling a similar workload load for the two groups. The results outline a peculiar neurofunctional profile of professional dancers, relying on intense cognitive anticipatory control and optimized proactive processing, allowing them superior response precision in sensory-motor performance. Further studies are needed to fully understand the specific trajectories of brain plasticity found here associated with dance practice.
Uncertainty-aware human-machine collaboration in Camouflaged Object Detection.
Yang Z, Wang K, Ming Y … +4 more , Yang H, Chen Q, Peng Y, Kong W
Cogn Neurodyn · 2026 Dec · PMID 41647134 · Full text
Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. We propose a human-machine collaboration fra...
Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. We propose a human-machine collaboration framework for COD, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. Evaluated on the CAMO dataset, our framework achieves state-of-the-art results with an average improvement of 4.56% in balanced accuracy (BA) and 3.66% in the F1 score. For the best-performing participants, improvements reached 7.6% in BA and 6.66% in the F1 score. Training analysis showed a strong correlation between confidence and precision, while ablation studies confirmed the effectiveness of our training policy and human-machine collaboration strategy. This work reduces human cognitive load, improves system reliability, and provides a foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.
Incorporating memristive autapse in spatio-temporal attention SNN for neuromorphic speech recognition.
Cheng Q, Chen T, Tang X … +2 more , Duan S, Wang L
Cogn Neurodyn · 2026 Dec · PMID 41647133 · Full text
Spiking neural networks (SNNs) have gained significant attention for their biological plausibility, event-driven operation, and low power consumption, establishing them as a leading model for processing event stream data...
Spiking neural networks (SNNs) have gained significant attention for their biological plausibility, event-driven operation, and low power consumption, establishing them as a leading model for processing event stream data. However, current models often oversimplify neuronal dynamics to balance computational cost and performance. To address this limitation and enhance the dynamical behavior of spiking neurons, this paper introduces two key innovations. First, inspired by biological autaptic connections and memristive devices, we propose the memristive autapse (M-Autapse), a self-connection mechanism that enables adaptive modulation of a neuron's membrane potential. Second, recognizing the need for attention mechanisms that match SNNs' spatio-temporal nature, we design a spatio-temporal synergistic attention (STSA) mechanism to bolster simultaneous focus on both temporal and spatial dimensions of input data. Extensive experiments on the neuromorphic speech benchmarks SHD and SSC validate our methods. On SHD, our model demonstrates performance competitive with the state-of-the-art, while also achieving strong results on the SSC dataset.
VaeTF-A community-aware perceptual architecture for detecting autism spectrum disorders using fMRI.
Fan Y, Ai L, Tian Y
Cogn Neurodyn · 2026 Dec · PMID 41613420 · Full text
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarc...
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarchical deep learning architecture, VaeTF, incorporating community-aware mechanisms based on resting-state functional magnetic resonance imaging (rs-fMRI) data. VaeTF introduces a priori knowledge of the functional community, extracts localized features through a variational auto-encoder (VAE), captures global dependencies across brain regions using the Transformer module, and incorporates an improved pooling mechanism to enhance the expressive power and model generalization performance. Experimental results on the ABIDE database show that VaeTF achieves 71.4% accuracy in ASD and typically performs well in group classification tasks. Further feature weighting analysis reveals that VaeTF is capable of identifying local functional abnormalities and cross-network functional synergistic dysfunctions closely related to ASD, thereby uncovering the underlying neurobiological mechanisms. VaeTF not only improves the classification performance of ASD but also provides a new method and theoretical support for objective assessment and early diagnosis based on fMRI.
Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.
Barranca VJ
Cogn Neurodyn · 2026 Dec · PMID 41458479 · Full text
Recent experiments have revealed that the inter-regional connectivity of the cerebral cortex exhibits strengths spanning over several orders of magnitude and decaying with distance. We demonstrate this to be a fundamenta...
Recent experiments have revealed that the inter-regional connectivity of the cerebral cortex exhibits strengths spanning over several orders of magnitude and decaying with distance. We demonstrate this to be a fundamental organizing feature that fosters high complexity in both connectivity structure and network dynamics, achieving an advantageous balance between integration and differentiation of information. This is verified through analysis of a multi-scale neuronal network model with nonlinear integrate-and-fire dynamics, incorporating inter-regional connection strengths decaying exponentially with spatial separation at the macroscale as well as small-world local connectivity at the microscale. Through numerical simulation and optimization over the model parameterspace, we show that inter-regional connectivity over intermediate spatial scales naturally facilitates maximally heterogeneous connection strengths, agreeing well with experimental measurements. In addition, we formulate complementary notions of structural and dynamical complexity, which are computationally feasible to calculate for large multi-scale networks, and we show that high complexity manifests for each over a similar parameter regime. We expect this work may help explain the link between distance-dependence in brain connectivity and the richness of neuronal network dynamics in achieving robust brain computations and effective information processing.
SC-SDT: a framework with spectral convolution and spatial differential transformer for EEG-based emotion recognition.
Li Q, Huang J, Zhou H
Cogn Neurodyn · 2026 Dec · PMID 41458478 · Full text
EEG signals are widely used in emotion recognition due to their capability for objective emotional state quantification. However, despite containing abundant frequency and spatial information, researchers continue to fac...
EEG signals are widely used in emotion recognition due to their capability for objective emotional state quantification. However, despite containing abundant frequency and spatial information, researchers continue to face challenges in extracting fine-grained discriminative features from these signals. We develop SC-SDT (Spectral Convolution-Spatial Differential Transformer), a novel framework that jointly models spectral and spatial characteristics through an integrated convolutional and transformer architecture. First the model is equipped with a Spectral Feature Embedding module that employs a sequential group-pointwise convolutional network. This enables the dynamic capture of both local spectral patterns within bands and global interactions across the frequency spectrum. Subsequently, a Spatial Feature Extraction module is designed to simultaneously mitigate attention noise and optimize functional connectivity mapping across EEG channels through its core differential attention mechanism. Finally, to enhance model robustness against inter-subject variability, we introduce supervised contrastive loss that explicitly enforces subject-invariant feature representations while preserving class discriminability. Employing a subject-independent experimental paradigm, we rigorously evaluated the proposed SC-SDT model on SEED, SEED-IV, and DEAP datasets to assess cross-subject generalization capabilities. Experimental results demonstrate that SC-SDT achieves competitive emotion classification performance by effectively modeling spectral-spatial neural signatures. Our analysis of its key components further reveals that the model not only pioneers the application of differential attention in EEG, but also offers a methodological foundation for efficient spectral-spatial feature extraction. The code for this paper is accessible at https://github.com/apolloCoder-byte/SC-SDT.
Synchronization characteristics of functional neurons under energy control.
Gu X, Zhang F, Liu Y … +3 more , Zhang M, Ge J, Jiang C
Cogn Neurodyn · 2026 Dec · PMID 41458477 · Full text
In biological neurons, synapses receive external stimuli to induce firing patterns. While the rapid generation of synapses regulates neural activity. In this paper, we use a magnetic-flux controlled memristor (MFCM) as a...
In biological neurons, synapses receive external stimuli to induce firing patterns. While the rapid generation of synapses regulates neural activity. In this paper, we use a magnetic-flux controlled memristor (MFCM) as a synapse to connect two functional neurons, establish the new coupled neurons, and study the synchronization characteristics. Firstly, we connect two neurons using memristive synapses, and derive the equations of the coupled neurons based on Kirchhoffs voltage law. Furthermore, we calculate the energy of the memristive coupling channels, and obtain the energy difference between the coupled neurons. Secondly, we propose a criterion for exponential growth controlled by energy difference. By setting higher coupling channel strength to establish synaptic connections, energy pumping can be effectively activated. Finally, for three modes, we analyze the energy evolution under the variations of memristive synapses, and find that the coupling channels are adaptively controlled by energy difference. The results show that when the coupling strength through synapses is enhanced, identical neurons can achieve complete synchronization, and different neurons can achieve phase locking. This study clarifies the underlying mechanisms of regulating coupled neurons via memristive synapses and explores how neurons achieve potential energy balance from the perspective of physical fields.
Correction: Quantifying harmony between direct and indirect pathways in the basal ganglia: healthy and Parkinsonian states.
Kim SY, Lim W
Cogn Neurodyn · 2026 Dec · PMID 41458476 · Full text
[This corrects the article DOI: 10.1007/s11571-024-10119-8.].
[This corrects the article DOI: 10.1007/s11571-024-10119-8.].
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