工程技术最新文献

Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task and a chaotic signal separation task involving Lorenz and Rössler time series. Analysis of network performance, correlation patterns, and weight matrices reveals that mutual information minimization yields high task performance alongside clear functional modularity and moderate structural modularity. Importantly, our results show that functional differentiation, which is measured through correlation structures, emerges earlier than structural modularity defined by synaptic weights. This suggests that functional specialization precedes and probably drives structural reorganization within developing neural networks. Our findings provide new insights into how information-theoretic principles may govern the emergence of specialized functions and modular structures during artificial and biological brain development.

This study investigates the anisotropic properties of three different poly(lactic-co-glycolic acid) (PLGA)-based materials: PLGA with nano-calcium sulphate (nCS), PLGA with fucoidan (fu) and PLGA with both nCS and fu. Using finite element analysis (FEA), the study explores their potential applications in bone tissue engineering. Anisotropy, or the directional dependency of mechanical properties, is critical in designing biomaterials for bone regeneration due to the complex, hierarchical structure of natural bone. The objective was to evaluate the mechanical behaviour of each composite material under simulated physiological conditions, focusing on their anisotropic responses to loading. The findings indicate that PLGA-nCS exhibited the highest degree of anisotropy, with enhanced stiffness and strength along preferred load-bearing directions, making it suitable for applications requiring higher mechanical stability. In contrast, PLGA-nCS-fu demonstrated moderate mechanical strength but displayed isotropic behaviour, ensuring consistent compressive performance across all directions. The study highlights the synergistic effects of incorporating nCS and fu into PLGA-based materials. fu, a natural sulphated polysaccharide derived from brown seaweed, significantly enhances the biological performance of these composites.

With the development of emotion recognition technology in various applications, studies based on EEG signals were carried out as they can directly reflect brain activity. Although existing graph neural network (GNN) methods have made some progress in processing EEG signals, they still face significant limitations in capturing complex spatiotemporal dependencies, avoiding over-smoothing, and handling cross-regional brain signal interactions, which impact the accuracy and robustness of emotion recognition. To address these problems, this paper proposes a Hierarchical Multi-Scale Graph Neural Network (HMSGNN). This method enhances the spatiotemporal feature modeling ability of EEG signals by extracting features at multiple levels, from local to global, thus improving the accuracy and robustness of emotion recognition. Experimental results show that HMSGNN achieves recognition accuracies of 98.67% and 85.72% in subject-dependent experiments on the SEED and SEED-IV datasets, and 87.11% and 76.14% in subject-independent experiments, respectively. Under the reproduced experimental settings, these values are the highest among the compared methods, while maintaining comparable or lower variance.

The processing of information within complex neural networks is a challenge topic that has intrigued researchers for many years. In this paper, we conducted an in-depth investigation into the learning mechanisms that are intrinsic to discrete memristor spiking neural networks. We also explored the effectiveness of information transmission and synchronization among various neurons and networks. Firstly, a memristor model with memory regulation function and tanh function's nonlinear characteristics was constructed. This model not only ensures that the internal state variables of the memristor do not exhibit divergence, but also demonstrates that this memristor is suitable for spiking signal processing and has the ability to transmit spiking signals. Secondly, our research delved into the intricate dynamics of these discrete spiking neural networks, including the ternary coupled spiking neural network and ring coupled spiking neural network, aiming to shed light on how they operate and interact. Thirdly, based on the designed pulse neurons, this study constructed a simple pulse neuron network. By reasonably setting the relevant parameters, the research found that this network possesses the ability for pattern recognition. The results of our investigation are crucial for understanding the mechanisms of information processing and synchronization phenomena within neural networks. It provides valuable insights into the potential of memristor networks in advancing artificial intelligence and computational neuroscience.

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.

Fast-spiking basket cells (FSBCs) govern hippocampal oscillations through their rapid and sustained firing patterns, which drive rhythmic inhibition onto postsynaptic neurons, thereby enforcing population synchrony in the gamma and other frequency bands that support cognitive processes. Despite the established role of FSBCs in hippocampal oscillations, the precise mechanisms by which their dendrites influence membrane potential responses across different frequency bands remain unclear. In this study, we simulate oscillation-like input protocols to explore how dendrites modulate the spectral responses of the membrane potentials of FSBCs. Our results show that FSBCs exhibit both slow and fast oscillatory components, which are shaped by their action potentials. Input synchrony is essential for determining both the fast-band response frequency and its coupling with the slow frequency, while the neuron's intrinsic firing dynamics maintain the stability of the fast-band peak frequency across theta-range inputs. Although dendritic Na[Formula: see text]/A-type K[Formula: see text] channel blockade and cp-AMPA enhancement both increase fast-band frequency, they differentially affect phase-amplitude coupling, with blockade reducing and cp-AMPA enhancement increasing it, highlighting the role of intrinsic dendritic conductances and cp-AMPA inputs in promoting coupling. Furthermore, we show that the spatial distribution of synaptic inputs along dendrites affects the response frequencies, with distinct frequencies observed at different dendritic locations according to their electrotonic distance. These findings provide insights into how the intrinsic properties of FSBCs influence their response to oscillatory inputs.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10418-2.

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.

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.

Diabetic nephropathy (DN), a major driver of end-stage kidney disease, elevates the risk for osteoporosis (OP) and its clinical precursor, low bone mineral density (low BMD), indicating broader systemic effects. While peripheral blood mononuclear cells (PBMCs) participate in both conditions, their common mechanisms remain poorly understood. This study aimed to identify common biomarkers and pathways linking DN to OP/low BMD by analyzing transcriptomic datasets from patients with these conditions. Using weighted gene co-expression network analysis (WGCNA), machine learning, and differential expression validation, we identified NLRP3 as a central hub gene. Functional analyses connected NLRP3 to pro-inflammatory pathways and immune cell activation. Single-cell data showed specific NLRP3 overexpression in DN patient macrophages, which exhibited heightened osteoclast differentiation capability. Protein analysis confirmed elevated NLRP3 levels in DN cases. In conclusion, PBMCs from DN patients with comorbid osteoporosis show upregulated NLRP3 expression and inflammasome activation, which may drive systemic inflammation and bone loss. These results clarify the pathological link between DN and OP/low BMD and highlight NLRP3 as a potential diagnostic marker and therapeutic target.

Pathogenic variants in the KIDINS220 gene can cause SINO syndrome (OMIM #617296), VENARG syndrome (OMIM #619501), or other neurological and metabolic disorders such as obesity and nystagmus. We identified two novel intronic variants in intron 29 of KIDINS220 gene (NM_020738.4), c.4054-2A > G and c.4054-7T > C, in a female patient presenting with motor dysfunction and developmental delay. Brain MRI revealed delayed myelination. To investigate whether these intronic variants cause aberrant splicing and affect protein expression, we sequenced KIDINS220 cDNA from peripheral blood and concurrently performed a minigene splicing assay. The results indicated that KIDINS220 was not expressed in PBMCs. However, the minigene assay demonstrated that the c.4054-2A > G variant causes an in-frame 336-bp deletion in exon 30, resulting in a 112-amino acid deletion in the C-terminal region of KIDINS220 (p.(Ser1352_Ser1463del)). In contrast, the c.4054-7T > C variant did not disrupt normal splicing. Based on the patient's clinical features and functional validation of the genetic variants, our paediatricians established a diagnosis of mild motor dysfunction and developmental delay. Our findings broaden the spectrum of pathogenic variants underlying KIDINS220-related disorders and provide essential information for genetic counselling.