International journal of neural systems最新文献

{"title":"Expanding Domain-Specific Datasets with Stable Diffusion Generative Models for Simulating Myocardial Infarction.","authors":"Gabriel Rojas-Albarracín, António Pereira, Antonio Fernández-Caballero, María T López","doi":"10.1142/S0129065725500522","DOIUrl":"10.1142/S0129065725500522","url":null,"abstract":"<p><p>Areas, such as the identification of human activity, have accelerated thanks to the immense development of artificial intelligence (AI). However, the lack of data is a major obstacle to even faster progress. This is particularly true in computer vision, where training a model typically requires at least tens of thousands of images. Moreover, when the activity a researcher is interested in is far from the usual, such as falls, it is difficult to have a sufficiently large dataset. An example of this could be the identification of people suffering from a heart attack. In this sense, this work proposes a novel approach that relies on generative models to extend image datasets, adapting them to generate more domain-relevant images. To this end, a refinement to stable diffusion models was performed using low-rank adaptation. A dataset of 100 images of individuals simulating infarct situations and neutral poses was created, annotated, and used. The images generated with the adapted models were evaluated using learned perceptual image patch similarity to test their closeness to the target scenario. The results obtained demonstrate the potential of synthetic datasets, and in particular the strategy proposed here, to overcome data sparsity in AI-based applications. This approach can not only be more cost-effective than building a dataset in the traditional way, but also reduces the ethical concerns of its applicability in smart environments, health monitoring, and anomaly detection. In fact, all data are owned by the researcher and can be added and modified at any time without requiring additional permissions, streamlining their research.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550052"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786192","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":"A Performance Benchmarking Review of Transformers for Speaker-Independent Speech Emotion Recognition.","authors":"Francisco Portal, Javier De Lope, Manuel Graña","doi":"10.1142/S0129065725300013","DOIUrl":"10.1142/S0129065725300013","url":null,"abstract":"<p><p>Speech Emotion Recognition (SER) is becoming a key element of speech-based human-computer interfaces, endowing them with some form of empathy towards the emotional status of the human. Transformers have become a central Deep Learning (DL) architecture in natural language processing and signal processing, recently including audio signals for Automatic Speech Recognition (ASR) and SER. A central question addressed in this paper is the achievement of speaker-independent SER systems, i.e. systems that perform independently of a specific training set, enabling their deployment in real-world situations by overcoming the typical limitations of laboratory environments. This paper presents a comprehensive performance evaluation review of transformer architectures that have been proposed to deal with the SER task, carrying out an independent validation at different levels over the most relevant publicly available datasets for validation of SER models. The comprehensive experimental design implemented in this paper provides an accurate picture of the performance achieved by current state-of-the-art transformer models in speaker-independent SER. We have found that most experimental instances reach accuracies below 40% when a model is trained on a dataset and tested on a different one. A speaker-independent evaluation combining up to five datasets and testing on a different one achieves up to 58.85% accuracy. In conclusion, the SER results improved with the aggregation of datasets, indicating that model generalization can be enhanced by extracting data from diverse datasets.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2530001"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736404","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":"Unsupervised Brain MRI Anomaly Detection via Inter-Realization Channels.","authors":"Hussain Ahmad Madni, Hafsa Shujat, Axel De Nardin, Silvia Zottin, Gian Luca Foresti","doi":"10.1142/S0129065725500479","DOIUrl":"10.1142/S0129065725500479","url":null,"abstract":"<p><p>Accurate anomaly detection in brain Magnetic Resonance Imaging (MRI) is crucial for early diagnosis of neurological disorders, yet remains a significant challenge due to the high heterogeneity of brain abnormalities and the scarcity of annotated data. Traditional one-class classification models require extensive training on normal samples, limiting their adaptability to diverse clinical cases. In this work, we introduce MadIRC, an unsupervised anomaly detection framework that leverages Inter-Realization Channels (IRC) to construct a robust nominal model without any reliance on labeled data. We extensively evaluate MadIRC on brain MRI as the primary application domain, achieving a localization AUROC of 0.96 outperforming state-of-the-art supervised anomaly detection methods. Additionally, we further validate our approach on liver CT and retinal images to assess its generalizability across medical imaging modalities. Our results demonstrate that MadIRC provides a scalable, label-free solution for brain MRI anomaly detection, offering a promising avenue for integration into real-world clinical workflows.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550047"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510084","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":"A Multivariate Cloud Workload Prediction Method Integrating Convolutional Nonlinear Spiking Neural Model with Bidirectional Long Short-Term Memory.","authors":"Minglong He, Nan Zhou, Hong Peng, Zhicai Liu","doi":"10.1142/S0129065725500716","DOIUrl":"https://doi.org/10.1142/S0129065725500716","url":null,"abstract":"<p><p>Multivariate workload prediction in cloud computing environments is a critical research problem. Effectively capturing inter-variable correlations and temporal patterns in multivariate time series is key to addressing this challenge. To address this issue, this paper proposes a convolutional model based on a Nonlinear Spiking Neural P System (ConvNSNP), which enhances the ability to process nonlinear data compared to conventional convolutional models. Building upon this, a hybrid forecasting model is developed by integrating ConvNSNP with a Bidirectional Long Short-Term Memory (BiLSTM) network. ConvNSNP is first employed to extract temporal and cross-variable dependencies from the multivariate time series, followed by BiLSTM to further strengthen long-term temporal modeling. Comprehensive experiments are conducted on three public cloud workload traces from Alibaba and Google. The proposed model is compared with a range of established deep learning approaches, including CNN, RNN, LSTM, TCN and hybrid models such as LSTNet, CNN-GRU and CNN-LSTM. Experimental results on three public datasets demonstrate that our proposed model achieves up to 9.9% improvement in RMSE and 11.6% improvement in MAE compared with the most effective baseline methods. The model also achieves favorable performance in terms of MAPE, further validating its effectiveness in multivariate workload prediction.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550071"},"PeriodicalIF":6.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194256","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":"Evolutionary Channel Pruning for Style-Based Generative Adversarial Networks.","authors":"Yixia Zhang, Ferrante Neri, Xilu Wang, Pengcheng Jiang, Yu Xue","doi":"10.1142/S0129065725500704","DOIUrl":"https://doi.org/10.1142/S0129065725500704","url":null,"abstract":"<p><p>Generative Adversarial Networks (GANs) have demonstrated remarkable success in high-quality image synthesis, with StyleGAN and its successor, StyleGAN2, achieving state-of-the-art performance in terms of realism and control over generated features. However, the large number of parameters and high floating-point operations per second (FLOPs) hinder real-time applications and scalability, posing challenges for deploying these models in resource-constrained environments such as edge devices and mobile platforms. To address this issue, we propose Evolutionary Channel Pruning for StyleGANs (ECP-StyleGANs), a novel algorithm that leverages evolutionary algorithms to compress StyleGAN and StyleGAN2 while maintaining competitive image quality. Our approach encodes pruning configurations as binary masks on the model's convolutional channels and iteratively refines them through selection, crossover, and mutation. By integrating carefully designed fitness functions that balance model complexity and generation quality, ECP-StyleGANs identifies optimally pruned architectures that reduce computational demands without compromising visual fidelity, achieving approximately a 4 × reduction in FLOPs and parameters, while maintaining visual fidelity with only a slight increase in FID (Fréchet Inception Distance) compared to the original un-pruned model. This study should be interpreted as a preliminary step towards the formulation and management of the generative AI pruning problem as a multi-objective optimisation task, aimed at enhancing the trade-off between model efficiency and image quality, thereby making large deep models more accessible for real-world applications such as edge devices and resource-constrained environments. <b>Source codes will be available.</b></p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550070"},"PeriodicalIF":6.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194195","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":"A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.","authors":"Wei Huang, Hengjiang Li, Fan Qin, Diwei Wu, Kaiwen Cheng, Huafu Chen","doi":"10.1142/S0129065725500686","DOIUrl":"https://doi.org/10.1142/S0129065725500686","url":null,"abstract":"<p><p>Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550068"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180045","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":"Driver Emotion Recognition Using Multimodal Signals by Combining Conformer and Autoformer.","authors":"Weiguang Wang, Jian Lian, Chuanjie Xu","doi":"10.1142/S0129065725500698","DOIUrl":"https://doi.org/10.1142/S0129065725500698","url":null,"abstract":"<p><p>This study aims to develop a multimodal driver emotion recognition system that accurately identifies a driver's emotional state during the driving process by integrating facial expressions, ElectroCardioGram (ECG) and ElectroEncephaloGram (EEG) signals. Specifically, this study proposes a model that employs a Conformer for analyzing facial images to extract visual cues related to the driver's emotions. Additionally, two Autoformers are utilized to process ECG and EEG signals. The embeddings from these three modalities are then fused using a cross-attention mechanism. The integrated features from the cross-attention mechanism are passed through a fully connected layer and classified to determine the driver's emotional state. The experimental results demonstrate that the fusion of visual, physiological and neurological modalities significantly improves the reliability and accuracy of emotion detection. The proposed approach not only offers insights into the emotional processes critical for driver assistance systems and vehicle safety but also lays the foundation for further advancements in emotion recognition area.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550069"},"PeriodicalIF":6.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139866","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":"Objective Assessment of Disorders of Consciousness Based on EEG Temporal and Spectral Features.","authors":"Wanqing Dong, Yi Yang, Tong Wu, Xiaorong Gao, Yanfei Lin, Jianghong He","doi":"10.1142/S0129065725500674","DOIUrl":"https://doi.org/10.1142/S0129065725500674","url":null,"abstract":"<p><p>Most existing studies analyzed the resting-state electroencephalogram (EEG) of DOC patients, and recent research demonstrated that the passive auditory paradigm was helpful for bedside detection of DOC and better captured sensory and cognitive responses. However, further studies of classification algorithms were needed for consciousness assessment in DOC based on task-state EEG data. In this study, EEG data from minimally conscious state (MCS) patients, vegetative state (VS) patients, and a healthy control group (HC) were collected using an auditory oddball paradigm. First, compared to the fragmented features adopted by most studies, multiple effective biomarkers for consciousness assessment in the time-frequency domains, connectivity and nonlinear dynamics were identified. Event-related potentials (ERP) results showed that MCS and VS patients exhibited lower N100 and MMN amplitudes than the HC group. Spectral analysis results indicated that VS patients had higher Delta power, and lower Alpha and Beta power than the MCS and HC groups. Second, different from insufficient classifiers in previous studies, this study systematically compared the performance of multiple machine learning and deep learning (DL) classifiers, including support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), eXtreme Gradient Boosting (XGBoost), decision tree (DT), EEGNet and ShallowConvNet. For machine learning methods, SVM and RF had an advantage in binary classification, and SVM had better performance in three-class classification. Among all individual classifiers, Shallow ConvNet had the best performance for binary and three-class classification. Moreover, an ensemble model incorporating all seven classifiers was proposed using a voting strategy, and further improved classification performance that was superior to existing studies. In addition, the importance of each feature was analyzed, identifying N100, MMN, Delta, Alpha, and Beta power as significant biomarkers of consciousness assessment.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550067"},"PeriodicalIF":6.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126875","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":"Brain Connectivity Variability Influences Anxiety Through the Behavioral Inhibition System.","authors":"Runyang He, Yan Zhu, Jiayu Ye, Dezhong Yao, Peng Xu, Fali Li, Lin Jiang, Yi Liang","doi":"10.1142/S0129065725500558","DOIUrl":"https://doi.org/10.1142/S0129065725500558","url":null,"abstract":"<p><p>The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550055"},"PeriodicalIF":6.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088752","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":"A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms.","authors":"Ferrante Neri, Mengchen Yang, Yu Xue","doi":"10.1142/S0129065725500650","DOIUrl":"https://doi.org/10.1142/S0129065725500650","url":null,"abstract":"<p><p>In the context of neural system structure modeling and complex visual tasks, the effective integration of multi-scale features and contextual information is critical for enhancing model performance. This paper proposes a biologically inspired hybrid neural network architecture - CompEyeNet - which combines the global modeling capacity of transformers with the efficiency of lightweight convolutional structures. The backbone network, multi-attention transformer backbone network (MATBN), integrates multiple attention mechanisms to collaboratively model local details and long-range dependencies. The neck network, compound eye neck network (CENN), introduces high-resolution feature layers and efficient attention fusion modules to significantly enhance multi-scale information representation and reconstruction capability. CompEyeNet is evaluated on three authoritative medical image segmentation datasets: MICCAI-CVC-ClinicDB, ISIC2018, and MICCAI-tooth-segmentation, demonstrating its superior performance. Experimental results show that compared to models such as Deeplab, Unet, and the YOLO series, CompEyeNet achieves better performance with fewer parameters. Specifically, compared to the baseline model YOLOv11, CompEyeNet reduces the number of parameters by an average of 38.31%. On key performance metrics, the average Dice coefficient improves by 0.87%, the Jaccard index by 1.53%, Precision by 0.58%, and Recall by 1.11%. These findings verify the advantages of the proposed architecture in terms of parameter efficiency and accuracy, highlighting the broad application potential of bio-inspired attention-fusion hybrid neural networks in neural system modeling and image analysis.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550065"},"PeriodicalIF":6.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115668","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}