Cognitive Neurodynamics最新文献

Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network. The TSO method incorporates information from multiple time steps into the selection strategy of the Best Matching Unit (BMU) neurons. It enables the coupled BMUs to radiate the weight across the same layer of neurons, ultimately forming a hierarchical self-organizing topographic map of concern. Additionally, we simulate real neuronal dynamics, introduce a glial cell-mediated Glial-LIF (Leaky Integrate-and-fire) model, and adjust multiple levels of BMUs to optimize the attention topological map.Experiments demonstrate that the proposed Self-organizing Glial Spiking Neural Network (SG-SNN) can generate attention topographies for dynamic event data from coarse to fine. A heuristic method based on cognitive science effectively guides the network's distribution of excitatory regions. Furthermore, the SG-SNN shows improved accuracy on three standard neuromorphic datasets: DVS128-Gesture, CIFAR10-DVS, and N-Caltech 101, with accuracy improvements of 0.3%, 2.4%, and 0.54% respectively. Notably, the recognition accuracy on the DVS128-Gesture dataset reaches 99.3%, achieving state-of-the-art (SOTA) performance.

A free calcium ion in the cytosol is essential for many physiological and physical functions. Also, it is known as a second messenger as the quantity of free calcium ions is an essential part of brain signaling. In this work, we have attempted to study calcium signaling in the presence of mitochondria, buffer, and endoplasmic reticulum fluxes. Small organelles called mitochondria are found in the nervous system and are involved in several cellular functions, including energy production, response to stress, calcium homeostasis regulation, and pathways leading to cell death. It has been discovered that buffer, endoplasmic reticulum, and mitochondria significantly affect calcium signaling. To investigate how various circumstances impact the quantity of calcium in the cytosol, a mathematical model of a second-order linear partial differential equation with fuzzy boundary conditions has been developed. Systems having ambiguous or imprecise boundary values can be effectively modeled and simulated with the help of fuzzy boundary conditions. Models can provide more dependable and instructive outcomes and become adaptable to real-world circumstances by implementing fuzzy logic into boundary conditions. In this paper, we observed the Fuzzy Laplace Transform to solve variable coefficient fuzzy differential equations using triangular fuzzy numbers. It is noted that maintaining the delicate calcium ion balance, which controls essential cellular functions, depends on the buffer affinity. Also, neurodegenerative illnesses like Alzheimer's, Parkinson's, etc. are linked to disruptions in the control of components such as buffers, mitochondria, and the endoplasmic reticulum.

Acupuncture has been widely used as an effective treatment for post-stroke rehabilitation. However, the potential association between acupuncture sensation, an important factor influencing treatment efficacy, and brain functional network is unclear. This research sought to reveal and quantify the changes in brain functional network associated with acupuncture sensation. So multi-channel EEG signals were collected from 30 healthy participants and the Massachusetts General Hospital Acupuncture Sensation Scale (MASS) was utilized to assess their needling sensations. Phase Lag Index (PLI) was used to construct the brain functional network, which was analyzed with graph theoretic methods. It showed that in the needle insertion (NI) state the MASS Index was significantly higher than in the needle retention (NR) state (P < 0.001), and the mean values of PLI were also higher than in the Pre-Rest state and NR state significantly (P < 0.01). In the NI state global efficiency, local efficiency, nodal efficiency, and degree centrality were significantly higher than in the Pre-Rest state and the NR state (P < 0.05), while the opposite is true for the shortest path length (P < 0.01). Then Pearson correlation analysis showed a correlation between MASS Index and graph theory metrics (P < 0.05). Finally, Support Vector Regression (SVR) was used to predict the MASS Index with a minimum mean absolute error of 0.65. These findings suggest that the NI state of acupuncture treatment changes the structure of the brain functional network and affects the graph theory metrics of the brain functional network, which may be an objective biomarker for quantitative evaluation of acupuncture sensation.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10233-1.

The multiple generalized stability of nonlinear systems with impulsive disturbance and distributed delays is studied in this paper. By using the state space partition method, the number of multiple equilibrium points for n-dimensional system is given by ${\prod }_{i=1}^n(2K_i+1)$ with integer $K_i\ge 0$ , and the sufficient conditions for generalized stability of ${\prod }_{i=1}^n(K_i+1)$ equilibrium points are derived. Finally, the theoretical results are illustrated by using the simulations of an example.

In this work, two capacitors connected by a thermistor are used to explore the electrical property of double-layer membrane in a neuron, which the membrane property is sensitive to changes of temperature and two capacitive variables are used to measure the potentials of inner and outer membrane. The circuit characteristics and energy definition for the neural circuit and its equivalent neuron model in oscillator form are clarified from physical aspect. Considering the shape deformation of cell membrane under external physical stimuli and energy injection, intrinsic parameters of the neuron can be controlled with adaptive growth under energy flow, an adaptive control law is proposed to regulate the firing modes accompanying with energy shift. In presence of noisy excitation, coherence resonance can be induced and confirmed by taming the noise intensity carefully. The distributions of CV (coefficient variability) and average energy value < H > vs. noise intensity provide a feasible way to predict the coherence resonance and even stochastic resonance in the neural activities. Adaptive parameter observers are designed to identify the unknown parameters in this neuron model. The research findings of this study lay a foundation for the design of temperature-adaptive biomimetic neuromorphic devices and the research on multi-functional perception neural networks with temperature sensitivity.

Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals during unconscious periods of sleep. Detailed study of CAP can help in early diagnosis of many sleep disorders. Firstly, the CAP cycles need to be segregated into their constituents, phase A and phase B periods. In this work, we develop an accurate and easy-to-implement system to distinguish between the two CAP phases. The EEG signals are denoised and divided into smaller segments for an easier processing. These segments are decomposed into different frequency sub-bands using zero-phase filtering. Thereafter, statistical features are extracted from the sub-band components, and significant features are selected using the Kruskal-Wallis test. We consider four different algorithms for classification, namely, k-nearest neighbour (kNN), support vector machine (SVM), bagged tree (BT) and neural network (NN). The classification results are compiled for the datasets that include healthy subjects and those suffering from insomnia. The BT classifier produces the best results for the combined balanced dataset, with 83.29% accuracy and 83.58% F-1 score. The proposed method is more accurate and efficient than the existing schemes and can be considered for widespread deployments in real-world scenarios.

Although deep brain stimulation (DBS) is effective in treating Parkinson's disease (PD) related to bursting, the underlying mechanisms remain unclear. In the present paper, the dynamical and synaptic mechanisms are studied in a basal ganglia-thalamus model. Firstly, slow and large oscillations of synaptic gating variables/currents are identified as the cause of the irregular and non-synchronous bursting for PD, indicating that interruption of these slow modulations may be a feasible measure to treat PD. Secondly, strong DBS with high frequency applied to subthalamic nucleus (STN) can induce fast synchronous spiking in both STN and external globus pallidus (GPe), then interrupt the slow gating variables, thereby eliminating the irregular bursting. Meanwhile, the gating variables of the excitatory and inhibitory synapses respectively from STN and GPe to the internal globus pallidus (GPi) become fast. Finally, competition between these two opposite synapses can induce two manners to eliminate the bursting of GPi and restore the normal state, appearing in vast majority of parameter space composed of multiple synaptic conductances. One is the synchronous silence of GPi, and the other the synchronous regular fast spiking, which occurs for large conductance of the inhibitory and excitatory synapse, respectively. Both result in regular spiking of thalamus, via interrupting slow gating variables of synapse projected to thalamus. In addition, as the two conductances approach each other, the synaptic current to GPi oscillates around zero slowly, resulting in irregular firings of GPi and thalamus for PD in a narrow parameter space. Furthermore, the bursting observed in PD before DBS and three types of electrical activities of GPi during DBS are explained, using a saddle-node bifurcation of limit cycles and oscillation patterns of synaptic current. The distinction from the post inhibitory rebound bursting reported in previous studies is discussed. The results present the mechanisms for DBS to treat PD via eliminating bursting in wide parameter region.

Cognitive load refers to the mental effort required to process information and perform tasks, significantly influencing learning and performance outcomes. This paper presents a novel approach for cognitive load classification using a hybrid model that integrates Long Short-Term Memory (LSTM) networks with the Block Attention Module (BAM). Leveraging functional Near-Infrared Spectroscopy (fNIRS), we investigate the relationship between cognitive load and brain activity in a controlled experimental setting. Our methodology encompasses data collection from 50 participants engaged in various problem-solving tasks, with cognitive load categorized as high, medium, or low. The acquired fNIRS data underwent a rigorous preprocessing pipeline, including normalization and wavelet transform for feature extraction, enabling a comprehensive analysis of hemodynamic responses. The proposed model employs BAM to enhance feature representation by refining the importance of spatial and channel dimensions, thus improving the LSTM's ability to capture temporal dependencies in the data. The experimental results demonstrate significant performance improvements in cognitive load classification, showcasing the efficacy of the integrated LSTM-BAM architecture. This work not only contributes to the understanding of cognitive load dynamics but also highlights the potential of fNIRS as a non-invasive tool for real-time monitoring of cognitive performance, paving the way for advancements in instructional design and cognitive research.

Driver fatigue is a major contributor to traffic accidents, leading to increased fatality rates and severe damage compared to incidents involving alert drivers. Electroencephalography (EEG) has emerged as a widely used method for detecting driver fatigue due to its ability to capture brain activity patterns. This survey provides a thorough analysis of devices that detect driver fatigue using EEG, analyzing existing methodologies, challenges, and future research directions. This study was carried out according to PRISMA criteria. Relevant studies were retrieved from SpringerLink, Web of Science, IEEE Xplore, Scopus, and ScienceDirect, covering research published until February 16, 2025. After 267 publications were identified, 87 scientific papers were fully analyzed based on their relevance and contribution to the identification of driver fatigue using EEG. The review explores the article selection process, followed by an in-depth discussion of driver fatigue detection systems across various domains. Applications of Machine Learning (ML) in EEG-based fatigue evaluation are carefully reviewed, covering data collection, preliminary processing, feature extraction, categorization techniques, and performance assessment. Additionally, a comparative evaluation of cutting-edge research provides a comprehensive visualization of current research trends. This survey highlights the advantages, limitations, and future prospects of EEG-based driver fatigue detection, offering valuable insights for improving road safety. The findings contribute to the development of more reliable and real-time fatigue detection systems by addressing existing challenges and recommending potential solutions.

Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ t-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.