International journal of neural systems最新文献

{"title":"Graph Spectral Analysis Using Electroencephalography in Alzheimer Disease and Frontotemporal Dementia Patients.","authors":"María Paula Bonomini, Eduardo Ghiglioni, Noelia Belén Ríos","doi":"10.1142/S0129065725500480","DOIUrl":"https://doi.org/10.1142/S0129065725500480","url":null,"abstract":"<p><p>Graph theory has proven to be useful in studying brain dysfunction in Alzheimer's disease using MagnetoEncephaloGraphy (MEG) and fMRI signals. However, it has not yet been tested enough with reduced sets of electrodes, as in the 10-20 EEG. In this paper, we applied techniques from the Graph Spectral Analysis (GSA) derived from EEG signals of patients with Alzheimer, Frontotemporal Dementia and control subjects. A collection of global GSA metrics were computed, accounting for general properties of the adjacency or Laplacian matrices. Also, regional GSA metrics were calculated, disentangling centrality measures in five cortical regions (frontal, central, parietal, temporal and occipital). These two sort of measures were then utilized in a binary AD/controls classification problem to test their utility in AD diagnosis and identify most valuable parameters. The Theta band appeared as the most connected and synchronizable rhythm for all three groups. Also, it was the rhythm with most preserved connections among temporal electrodes, exhibiting the shortest average distances among [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. In addition, Theta emerged as the rhythm with the highest classification performances based on regional parameters according to a [Formula: see text] cross-validation scheme (mean [Formula: see text], mean [Formula: see text] and mean <i>F</i>1-[Formula: see text]). In general, regional parameters produced better classification performances for most of the rhythms, encouraging further investigation into GSA parameters with refined spatial and functional specificity.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 9","pages":"2550048"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Dementia Diagnosis Through Distance-Correlation Feature Space and Dimensionality Reduction.","authors":"Pablo Zubasti, Miguel A Patricio, Antonio Berlanga, Jose M Molina","doi":"10.1142/S012906572550042X","DOIUrl":"10.1142/S012906572550042X","url":null,"abstract":"<p><p>The reduction of dimensionality in machine learning and artificial intelligence problems constitutes a pivotal element in the simplification of models, significantly enhancing both their performance and execution time. This process enables the generation of results more rapidly while also facilitating the scalability and optimization of systems that rely on such models. Two primary approaches are commonly employed to achieve dimensionality reduction: feature selection-based methods and those grounded in feature extraction. In this paper, we propose a distance-correlation feature space, upon which we define a dimensionality reduction algorithm based on space transformations and graph embeddings. This methodology is applied in the context of dementia diagnosis through learning models, with the overarching objective of optimizing the diagnostic process.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550042"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.","authors":"Juan A Barios, Yolanda Vales, Jose M Catalán, Andrea Blanco-Ivorra, David Martínez-Pascual, Nicolás García-Aracil","doi":"10.1142/S0129065725500443","DOIUrl":"https://doi.org/10.1142/S0129065725500443","url":null,"abstract":"<p><p>Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 9","pages":"2550044"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive EEG Emotion Recognition with Incremental Gaussian Processes.","authors":"Xiangle Ping, Wenhui Huang","doi":"10.1142/S0129065725500418","DOIUrl":"10.1142/S0129065725500418","url":null,"abstract":"<p><p>Interactivity is crucial for enabling models to adjust and optimize based on user feedback, thereby enhancing overall performance. However, existing electroencephalogram (EEG)-based emotion recognition models rely on static training paradigms, lack interactivity, and struggle to effectively handle uncertainty in predictions. To address this issue, we propose a novel paradigm for interactive emotion recognition based on incremental Gaussian processes (GP). Unlike existing methods, our approach introduces an expert interaction mechanism to correct samples with high predictive uncertainty and incrementally update the model accordingly, thereby optimizing its performance. First, we model the emotion recognition task as a GP-based framework, utilizing the variance of the GP to quantify the model's uncertainty, thereby guiding experts in targeted interactions. Second, within the GP framework, we propose a novel incremental update strategy that allows the GP to incrementally update prediction results and uncertainties based only on new data obtained through expert interactions, without reprocessing all existing data. This effectively overcomes the shortcomings of traditional GP in updating efficiency. Third, to address the high computational complexity of GP, we use a sparse approximation strategy, selecting inducing points and performing variational inference to efficiently approximate the GP posterior, thereby reducing computational complexity. Subject-dependent and subject-independent experiments conducted on the DEAP and DREAMER datasets demonstrate that the proposed method exhibits significant advantages over state-of-the-art (SOTA) methods. In subject-dependent experiments, our method achieved the highest improvement (1.73%) in the Dominance dimension on the DREAMER dataset. In subject-independent experiments, it attained the largest performance improvement (2.96%) in the Arousal dimension on the DEAP dataset. These results further validate the proposed method's effectiveness.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550041"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Compliance Utilization Method Based on Adaptive Differential Privacy and Federated Learning.","authors":"Haiyan Kang, Bing Wu, Chong Zhang","doi":"10.1142/S0129065725500601","DOIUrl":"https://doi.org/10.1142/S0129065725500601","url":null,"abstract":"<p><p>Federated learning (FL), as a method that coordinates multiple clients to train models together without handing over local data, is naturally privacy-preserving for data. However, there is still a risk that malicious attackers can steal intermediate parameters and infer the user's original data during the model training, thereby leaking sensitive data privacy. To address the above problems, we propose an adaptive differential privacy blockchain federated learning (ADP-BCFL) method to accomplish the compliant use of distributed data while ensuring security. First, utilize blockchain to accomplish secure storage and valid querying of user summary data. Second, propose an adaptive DP mechanism to be applied in the process of federal learning, which adaptively adjusts the threshold size of parameter tailoring according to the parameter characteristics, controls the amount of introduced noise, and ensures a good global model accuracy while effectively solving the problem of inference attack. Finally, the ADP-BCFL method was validated on the MNIST, Fashion MNIST datasets and spatiotemporal dataset to effectively balance model performance and privacy.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550060"},"PeriodicalIF":6.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight Diffusion Models Based on Multi-Objective Evolutionary Neural Architecture Search.","authors":"Yu Xue, Chunxiao Jiao, Yong Zhang, Ali Wagdy Mohamed, Romany Fouad Mansour, Ferrante Neri","doi":"10.1142/S0129065725500595","DOIUrl":"https://doi.org/10.1142/S0129065725500595","url":null,"abstract":"<p><p>Diffusion models have achieved remarkable success in image generation, image super-resolution, and text-to-image synthesis. Despite their effectiveness, they face key challenges, notably long inference time and complex architectures that incur high computational costs. While various methods have been proposed to reduce inference steps and accelerate computation, the optimization of diffusion model architectures has received comparatively limited attention. To address this gap, we propose LDMOES (<b>L</b>ightweight <b>D</b>iffusion Models based on <b>M</b>ulti-<b>O</b>bjective <b>E</b>volutionary <b>S</b>earch), a framework that combines multi-objective evolutionary neural architecture search with knowledge distillation to design efficient UNet-based diffusion models. By adopting a modular search space, LDMOES effectively decouples architecture components for improved search efficiency. We validated our method on multiple datasets, including CIFAR-10, Tiny-ImageNet, CelebA-HQ [Formula: see text], and LSUN-church [Formula: see text]. Experiments show that LDMOES reduces multiply-accumulate operations (MACs) by approximately 40% in pixel space while outperforming the teacher model. When transferred to the larger-scale Tiny-ImageNet dataset, it still generates high-quality images with a competitive FID score of 4.16, demonstrating strong generalization ability. In latent space, MACs are reduced by about 50% with negligible performance loss. After transferring to the more complex LSUN-church dataset, the model surpasses baselines in generation quality while reducing computational cost by nearly 60%, validating the effectiveness and transferability of the multi-objective search strategy. Code and models will be available at https://github.com/GenerativeMind-arch/LDMOES.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550059"},"PeriodicalIF":6.4,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Unfolded Variable Projection Networks.","authors":"Gergő Bognár, Manuel Feindert, Christian Huber, Michael Lunglmayr, Mario Huemer, Péter Kovács","doi":"10.1142/S0129065725500534","DOIUrl":"https://doi.org/10.1142/S0129065725500534","url":null,"abstract":"<p><p>In this paper, we present a hybrid learning framework that integrates two model-driven AI paradigms: Deep unfolding and Variable Projections (VPs). The core idea is to unfold the iterations of VP solvers for separable nonlinear least squares (SNLLS) problems into trainable neural network layers. As a consequence, the network is capable of learning optimal nonlinear VP parameters during inference, which is a form of model-based meta-learning. Furthermore, the architecture incorporates prior knowledge of the underlying SNLLS problem, such as basis function expansions and signal structure, which enhance interpretability, reduce model size, and lower data requirements. As a case study, we adapt the proposed deep unfolded VPNet to learn ECG representations for the classification of five arrhythmias. Experimental results on the MIT-BIH Arrhythmia Database show that VPNet achieves performance comparable to state-of-the-art ECG classifiers, attaining  95% accuracy while maintaining a compact architecture. Its low computational complexity enables efficient training and inference, making it highly suitable for real-time, power-efficient edge computing applications. This is further validated through embedded implementation on STM32 microcontrollers.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550053"},"PeriodicalIF":6.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Bio-Inspired Models under Different Learning Settings for Energy Efficiency in Network Traffic Prediction.","authors":"Theodoros Tsiolakis, Nikolaos Pavlidis, Vasileios Perifanis, Pavlos Efraimidis","doi":"10.1142/S0129065725500583","DOIUrl":"https://doi.org/10.1142/S0129065725500583","url":null,"abstract":"<p><p>Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algorithms have emerged as powerful tools for handling these large datasets and providing accurate predictions, their environmental impact, particularly in terms of energy consumption, is often overlooked in favor of their predictive capabilities. This study investigates the potential of two bio-inspired models: Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs) for cellular traffic forecasting. The evaluation focuses on both their predictive performance and energy efficiency. These models are implemented in both centralized and federated settings to analyze their effectiveness and energy consumption in decentralized systems. Additionally, we compare bio-inspired models with traditional architectures, such as Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs), to provide a comprehensive evaluation. Using data collected from three diverse locations in Barcelona, Spain, we examine the trade-offs between predictive accuracy and energy demands across these approaches. The results indicate that bio-inspired models, such as SNNs and ESNs, can achieve significant energy savings while maintaining predictive accuracy comparable to traditional architectures. Furthermore, federated implementations were tested to evaluate their energy efficiency in decentralized settings compared to centralized systems, particularly in combination with bio-inspired models. These findings offer valuable insights into the potential of bio-inspired models for sustainable and privacy-preserving cellular traffic forecasting.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550058"},"PeriodicalIF":6.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus-Based 3D View Generation from Noisy Images.","authors":"José A Rodríguez-Rodríguez, Miguel A Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio","doi":"10.1142/S0129065725500571","DOIUrl":"https://doi.org/10.1142/S0129065725500571","url":null,"abstract":"<p><p>The real-time synthesis of 3D views, facilitated by convolutional neural networks like NeX, is increasingly pivotal in various computer vision applications. These networks are trained using photographs taken from different perspectives during the training phase. However, these images may be susceptible to contamination from noise originating from the vision sensor or the surrounding environment. This research meticulously examines the impact of noise on the resulting image quality of 3D views synthesized by the NeX network. Various noise levels and scenes have been incorporated to substantiate the claim that the presence of noise significantly degrades image quality. Additionally, a new strategy is introduced to improve image quality by calculating consensus among NeX networks trained on images pre-processed with a denoising algorithm. Experimental results confirm the effectiveness of this technique, demonstrating improvements of up to 1.300 dB and 0.032 for Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), respectively, under certain scenes and noise levels. Notably, the performance gains are especially significant when using synthesized images generated by NeX from noisy inputs in the consensus process.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550057"},"PeriodicalIF":6.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global-Local Feature Fusion Network Based on Nonlinear Spiking Neural Convolutional Model for MRI Brain Tumor Segmentation.","authors":"Junjie Li, Hong Peng, Bing Li, Zhicai Liu, Rikong Lugu, Bingyan He","doi":"10.1142/S0129065725500364","DOIUrl":"10.1142/S0129065725500364","url":null,"abstract":"<p><p>Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doctors in diagnosis, treatment planning and surgical navigation. NSNP-like convolutional model is a new neural-like convolutional model inspired by nonlinear spiking mechanism of nonlinear spiking neural P (NSNP) systems. Therefore, this paper proposes a global-local feature fusion network based on NSNP-like convolutional model for MRI brain tumor segmentation. To this end, we have designed three characteristic modules that take full advantage of the NSNP-like convolution model: dilated SNP module (DSNP), multi-path dilated SNP pooling module (MDSP) and Poolformer module. The DSNP and MDSP modules are employed to construct the encoders. These modules help address the issue of feature loss and enable the fusion of more high-level features. On the other hand, the Poolformer module is used in the decoder. It processes features that contain global context information and facilitates the interaction between local and global features. In addition, channel spatial attention (CSA) module is designed at the skip connection between encoder and decoder to establish the long-range dependence between the same layers, thereby enhancing the relationship between channels and making the model have global modeling capabilities. In the experiments, our model achieves Dice coefficients of 85.71[Formula: see text], 92.32[Formula: see text], 87.75[Formula: see text] for ET, WT, and TC, respectively, on the N-BraTS2021 dataset. Moreover, our model achieves Dice coefficients of 83.91[Formula: see text], 91.96[Formula: see text], 90.14[Formula: see text] and 85.05[Formula: see text], 92.30[Formula: see text], 90.31[Formula: see text] on the BraTS2018 and BraTS2019 datasets respectively. Experimental results also indicate that our model not only achieves good brain tumor segmentation performance, but also has good generalization ability. The code is already available on GitHub: https://github.com/Li-JJ-1/NSNP-brain-tumor-segmentation.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550036"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}