Cognitive Neurodynamics最新文献

The complexity of neural dynamics heavily depends on the nonlinear activation functions, and a mixed-bipower activation function (MBPAF) with adjustable parameters is designed for the memristive Hopfield neural network (MHNN) to generate complex hyper-chaotic behaviors. Based on the designed MBPAF, a novel MBPAF-memristive Hopfield neural network (MBPAF-MHNN) model is proposed. The complex dynamics of the proposed MBPAF-MHNN model are validated through numerical analyses and further verified via FPGA implementation. Finally, a robust image encryption scheme is designed based on the MBPAF-MHNN model, featuring a plaintext-related "Diffusion-Permutation-Diffusion" architecture with DNA-based operations.

Numerous physiological observations have shown that the brain operates at the edge of a critical state between order and disorder. Meanwhile, brain structures at different scales, from cortical columns to the entire brain, are organized in a modular manner. However, whether modular brain networks represent the optimized structure shaped for criticality and in what ways, have not been fully answered. In this study, a modular network with dense intra-module links but sparse inter-module links is established, and the behavior of each neuron is governed by the Kinouchi-Copelli model. Moreover, randomized surrogate networks with identical degree distribution are introduced to illustrate the significance of modular structures for criticality. Results suggest that the modular network requires fewer synaptic resources and lower firing costs to achieve criticality. More importantly, smaller avalanches indicate that the modular structures can enhance network resilience, facilitating rapid recovery from perturbations. Furthermore, by testing the sensitivity of the network state to local excitatory-inhibitory fluctuations, it is found that the efficiency of excitatory and inhibitory regulation is closely related to the 2-level excitatory input density. In addition, inhibitory regulation targeting modules with larger maximum real eigenvalues can more effectively suppress hyperexcitatory activities to achieve balance. When local excitation is greatly enhanced, even if the modular network is adjusted to the critical state, the size-to-duration ratio of module-level avalanches can effectively capture abnormalities. The properties also manifest in clinical recordings from patients with temporal lobe epilepsy, which may provide a promising method for epileptogenic zone localization.

We introduce a brain-constrained neurocomputational model designed to simulate higher cognitive functions of the human brain, implemented using NEST, a widely used open-source simulator optimised for high-performance spiking neural network simulations. Previously implemented in the custom-built C-based Felix simulation library, transitioning the model to NEST enhances accessibility, reproducibility, and computational efficiency. At the cellular level, the model comprises spiking excitatory neurons and local inhibitory neurons, whereas at the network level, it replicates the structural and functional organisation of 12 cortical regions spanning frontal, temporal, and occipital cortices, along with their associated inter-area connectivity. Additionally, global inhibition mechanisms and neuronal noise are integrated. Learning in the model follows biologically plausible Hebbian plasticity principles, incorporating both long-term potentiation and long-term depression. To validate the NEST implementation, we replicated previous simulation findings obtained with the Felix-based model. The new implementation successfully reproduced the same topographical distribution of cell assemblies following associative learning of object and action words within action and perception systems, replicating a range of previous neuroimaging results. Although the NEST model produced larger cell assemblies than Felix, the overall topographical patterns remained similar, indicating preservation of fundamental network characteristics. Moreover, the transition to NEST significantly enhanced computational efficiency, reducing simulation runtime nearly sixfold compared to Felix. This improvement in computational speed is crucial for expanding the model to include additional cortical regions, such as extending to the right hemisphere, which necessitates increased computational resources.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10415-5.

Multi-label depressive emotion classification remains challenging due to the co-occurrence of multiple mental health-related emotions, implicit linguistic expressions, long-range contextual dependencies, and the presence of both active and passive depressive signals. This paper presents a comprehensive study on multi-label depressive emotion detection using the DepressionEmo dataset, which contains textual instances annotated with eight clinically relevant depression emotions: Anger, Cognitive Dysfunction, Emptiness, Hopelessness, Loneliness, Sadness, Suicide Intent and Worthlessness. The objective is to develop an effective and computationally efficient multi-label classification framework capable of accurately identifying both explicit active and latent passive depressive emotions from text. To address this problem, a broad spectrum of transformer-based and hybrid architectures is evaluated, including BERT, RoBERTa, DistilBERT, T5, BART, and DeBERTa with BiLSTM integration, as well as seq2seq BART and seq2seq RoBERTa-BART models. The proposed DeBERTa-BiLSTM architecture integrates disentangled self-attention for rich contextual representation with a BiLSTM layer for sequential dependency learning and history-state fusion, enabling effective modeling of long-range depressive cues. Experimental results demonstrate that the proposed DeBERTa-BiLSTM model consistently outperforms baseline seq2seq BART, BERT, T5, GAN-BERT, and all other developed variants, achieving an F1-Micro score of 0.83 and an F1-Macro score of 0.80, along with the lowest Hamming Loss (0.15) and the highest Jaccard Index (0.71). The model further achieves micro-precision of 0.81 and micro-recall of 0.85 indicating robust detection of both frequent and minority emotion labels. Runtime analysis shows notable inference efficiency, reducing time per sample by 26.32% at batch size 4 and 21.39% at batch size 32 compared to seq2seq BART. Despite these advantages, the model remains computationally heavier than lightweight transformers, is influenced by the dataset's modest size, and requires further validation across broader mental health domains.

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.

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.

This paper investigates the multistability of complex-valued neural networks (CVNNs) with state-dependent switching rules and discontinuous nonmonotonic piecewise linear activation functions featuring k peaks. By leveraging Brouwer's fixed point theorem and the properties of strictly diagonally dominant matrices, we analyze the existence, stability, and instability of equilibrium points through state space decomposition. Our results demonstrate that an n-neuron switching CVNNs can possess up to [Formula: see text] equilibrium points, among which [Formula: see text] are stable. These findings significantly extend existing results and enrich the stability theory of neural networks. Numerical examples validate the theoretical conclusions and illustrate potential applications in associative memory.

Detecting seizures automatically is crucial for diagnosing and treating epilepsy, substantially benefiting affected patients. Various deep learning models and methods have been developed to automatically extract features from electroencephalogram (EEG) data for detecting seizures, but may often fail to adequately capture the significant periodic and semi-periodic dynamics in EEG signals, thus incompletely representing the extracted features. To address this challenge, we here introduced a novel EEG feature learning framework named ContrLF. This framework combines a contrastive learning framework and the Floss method to improve EEG feature learning for epileptic seizure detection. In our methodology, initially, both strong and weak augmentation are applied to transform the original EEG data into two distinct yet correlated views. Then, Floss is employed to automatically detect and learn the primary periodic dynamics within the augmented EEG data, capturing meaningful periodic representations that are essential for understanding seizure patterns in EEG signals. In parallel, the augmented EEG data were sequentially processed through temporal and contextual contrasting modules, which are designed to learn robust feature representations of the EEG signals. Finally, a Support Vector Machine (SVM) classifier was used to evaluate the effectiveness of the EEG features extracted using our proposed framework. Experimental results generated using both scalp and intracranial electroencephalogram (iEEG) datasets revealed that the proposed framework achieves over 90% accuracy, sensitivity, and specificity in detecting seizures. The framework outperforms other state-of-the-art methods, demonstrating its superiority in both cross-patient and specific-patient seizure detection.

This study aims to elucidate astrocyte-mediated regulation of focal epileptic seizures and mechanisms underlying the development of epilepsy. To address this, we propose an improved cortical layer model to investigate the function and therapeutic value of astrocyte in neurological disorders. By analyzing the spatio-temporal characteristics of focal epilepsy seizures and their propagation, we find that high frequency inhibitory stimulation appeared to effectively delay or prevent seizures. In addition, the results suggest that different frequencies of Ca²⁺ oscillation and levels of coupling strengths have substantial effects on focal epilepsy. Based on experimental and clinical research findings, we develop a potential clinical application process for epilepsy development and delineate its implications for the possibility of postoperative epilepsy recurrence.

K-complexes (KCs), sleep-specific neuroprotective waveforms, demonstrate significant modulation by environmental noise (EN). However, the principles governing how EN modulates KCs occurrence remain poorly understood. To address this gap, we develop a stochastic neural dynamic model incorporating EN (SNDM-KCs) and explore the modulation effects of EN on KCs from the perspective of stochastic dynamics. The Gaussian colored noise (GCN) is first applied to model EN and introduced into the deterministic Costa neural mass model to build the SNDM-KCs. Next, bifurcation analysis is conducted to demonstrate that the prerequisite for occurrence of KCs corresponds to a large-amplitude departure from a stable equilibrium induced by GCN in the dynamic system. Subsequently, we study the impact of GCN on KCs by integrating SNDM-KCs with defined two metrics to quantitatively measure the elicitation variation of KCs. Numerical simulations suggest that both KCs occurrence probability and rate increase with noise intensity D and correlation rate [Formula: see text] of GCN. Meanwhile, building on stochastic escape theory, we establish the relationship between model behaviour and stochastic escape metrics: first escape probability (FEP) and the mean first exit time (MFET), to investigate how EN modulates KCs through the lens of stochastic dynamics. The results demonstrate that as the escape probability of the system rises, the occurrence probability of KC increases accordingly. Meanwhile, a shorter time to escape from the safe domain indicates a faster occurrence rate of KCs. Our work provides a novel dynamical insight for investigating the principles governing how EN modulates KCs occurrence.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10440-4.