International journal of neural systems最新文献_第6页

Quantitative EEG Evidences Reveal an Effective Approach for Regulating Negative Emotions via Listening Mindfulness-Based Music. 定量脑电图证据揭示了通过听正念音乐调节负面情绪的有效方法。

IF 6.4

International journal of neural systems Pub Date : 2026-09-01 Epub Date: 2026-04-08 DOI: 10.1142/S0129065726500280

Xinyi Xu, Hui Gao, Xiaoyu Li, Zhao Feng, Kuijun Wu, Linze Qian, Fo Hu, Hongtao Wang, Yu Sun

{"title":"Quantitative EEG Evidences Reveal an Effective Approach for Regulating Negative Emotions via Listening Mindfulness-Based Music.","authors":"Xinyi Xu, Hui Gao, Xiaoyu Li, Zhao Feng, Kuijun Wu, Linze Qian, Fo Hu, Hongtao Wang, Yu Sun","doi":"10.1142/S0129065726500280","DOIUrl":"10.1142/S0129065726500280","url":null,"abstract":"The detrimental effects of negative emotional states have prompted extensive research into developing effective emotion regulation strategies. Here, we introduced a standardized protocol via merging mindfulness prompts with soothing music for regulating negative emotions (i.e. sadness and fear) and investigated the underlying neural mechanisms during emotion regulation. Specifically, EEG data were obtained from 43 healthy participants (m/f = 18/25, age = 22.7 ± 2.2 yrs) throughout baseline (eyes-closed resting), emotion inducing (via watching sadness/fear film clips selected from SEED-IV dataset), and regulation (via listening customized mindfulness-based music) phases. Post-intervention questionnaire showed successful reduction of emotion-related negative valence and arousal. Following analyses revealed broad oscillatory dynamics across various frequency bands as well as network reorganizations, suggesting neural resource reallocation during regulation toward baseline. More importantly, the right-hemispheric lateralization of theta- and gamma-band power and theta-band global efficiency distinctions between sadness and fear highlighted emotion-specific regulatory pathways. In sum, these findings validate the effectiveness of the proposed synthesized mindfulness-music in emotion regulation and elucidate the corresponding spectro-topological reorganizations, paving the way for further exploratory investigations of personalized interventions targeting distinct negative emotions.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650028"},"PeriodicalIF":6.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635537","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}

引用次数: 0

A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task. 在快速连续视觉呈现任务中，一种少层多层感知器是值得关注的脑电分类方法。

IF 6.4

International journal of neural systems Pub Date : 2026-09-01 Epub Date: 2026-04-10 DOI: 10.1142/S0129065726500309

Ziyuan Zhang, Yang Zheng, Kaitai Guo, Jimin Liang, Minghao Dong

{"title":"A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task.","authors":"Ziyuan Zhang, Yang Zheng, Kaitai Guo, Jimin Liang, Minghao Dong","doi":"10.1142/S0129065726500309","DOIUrl":"10.1142/S0129065726500309","url":null,"abstract":"Rapid serial visual presentation (RSVP) enables efficient electroencephalography (EEG)-based brain-computer interfaces, yet single-trial decoding remains difficult due to signal overlap and multicomponent entanglement. This work developed DisCo-Former, a Transformer-based framework incorporating three priors-guided components, including trend-periodicity disentanglement, channel-level embeddings that preserve global temporal pattern, and contrastive learning that exploits target-adjacent nontargets. Although DisCo-Former surpassed existing approaches, analysis revealed a consistent attention collapse: attention maps became nearly uniform, and value projection weights shrank toward zero. Removing the Transformer encoder yields DisCo-MLP, a purely multilayer perceptron (MLP) variant that preserves all remaining modules. Across two datasets and three evaluation regimes, DisCo-MLP matched or outperformed its Transformer-based counterpart. In within-subject decoding, mean AUCs ranged from approximately 0.94 to 0.98 across two datasets, consistently exceeding strong baselines. These results indicate that, for RSVP-EEG decoding, effectiveness stems less from architectural complexity and more from modeling the signal's structure. Simplicity motivated by paradigm-specific neurophysiological priors offers a practical path to state-of-the-art performance in EEG-based interfaces.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650030"},"PeriodicalIF":6.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648006","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}

引用次数: 0

Multibranch Attention and Fusion Network for Voice-Based Autism Detection Using Heterogeneous Audio Embeddings. 基于异构音频嵌入的语音自闭症检测多分支注意融合网络。

IF 6.4

International journal of neural systems Pub Date : 2026-08-31 DOI: 10.1142/S012906572750016X

Mouad El Omari, Younes El Belghiti, Hanae Belmajdoub, Khalid Minaoui, Samir Saoudi

{"title":"Multibranch Attention and Fusion Network for Voice-Based Autism Detection Using Heterogeneous Audio Embeddings.","authors":"Mouad El Omari, Younes El Belghiti, Hanae Belmajdoub, Khalid Minaoui, Samir Saoudi","doi":"10.1142/S012906572750016X","DOIUrl":"https://doi.org/10.1142/S012906572750016X","url":null,"abstract":"Voice-based analysis is attracting growing interest as a noninvasive means of identifying early markers of autism spectrum disorder (ASD). While pretrained audio models such as YAMNet and VGGish provide complementary views of children's speech, most existing studies rely on a single representation and do not explore how these embeddings may be combined in a structured manner. This work introduces the multibranch attention and fusion network (MBAFNet), an architecture designed to make fuller use of heterogeneous embeddings by processing each stream through its own convolutional encoder, extracting temporal cues at multiple scales, and modeling cross-representation interactions through a self-attention layer. A gating mechanism then regulates the relative contribution of each embedding before classification. Experiments conducted on the CASD-SC corpus under a subject-disjoint five-fold evaluation protocol show that MBAFNet achieves 94.17% accuracy, outperforming all evaluated baselines and previously reported state-of-the-art approaches. These findings indicate that carefully designed selective fusion can reveal complementary information contained in pretrained embeddings and supports the development of more robust speech-based ASD assessment frameworks, while broader validation remains necessary before screening-oriented use.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750016"},"PeriodicalIF":6.4,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868502","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}

引用次数: 0

Dual-Track Interactive-Fusion Directed Graph Neural Network for Entity Identification in Information Propagation. 信息传播中实体识别的双轨交互融合有向图神经网络。

IF 6.4

International journal of neural systems Pub Date : 2026-08-31 DOI: 10.1142/S0129065727500171

Xuna Wang, Qingmei Tan

{"title":"Dual-Track Interactive-Fusion Directed Graph Neural Network for Entity Identification in Information Propagation.","authors":"Xuna Wang, Qingmei Tan","doi":"10.1142/S0129065727500171","DOIUrl":"https://doi.org/10.1142/S0129065727500171","url":null,"abstract":"This study addresses the entity identification problem in information propagation networks and proposes a novel dual-track interactive-fusion directed graph neural network (GNN). The method characterizes node features from two complementary perspectives: static topology and dynamic path dependency. On the one hand, it considers the relative position of nodes in directed network path sequences and enhances the model's ability to represent directed structural relations through static-track encoding. On the other hand, dynamic-track encoding is used to learn the sequential associations among nodes in network path sequences. Based on this, a bidirectional cross-track interactive fusion mechanism is introduced to facilitate the interaction, fusion, and collaborative enhancement of latent information between the static and dynamic tracks, thereby improving node representations in directed graphs. Experiments on the Cora, Reddit, PubMed, and Ogbn-arxiv datasets show that, compared with the best baseline, the proposed dual-track interactive-fusion method improves the F1-score by 1.83%, 3.32%, 3.19%, and 2.61%, respectively. The corresponding reduction rates in the remaining performance gap are 5.93%, 11.98%, 13.98%, and 8.55%, respectively. This study provides a new theoretical perspective and technical approach for handling complex dynamic directed graphs and has important application value in social media analysis, public opinion monitoring, and network security.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750017"},"PeriodicalIF":6.4,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868481","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}

引用次数: 0

Enhancing Neural Encoding of Natural Scenes through Hierarchical Integration of Saliency and Semantic Context. 通过显著性和语义上下文的层次整合增强自然场景的神经编码。

IF 6.4

International journal of neural systems Pub Date : 2026-08-29 DOI: 10.1142/S0129065727500201

Sizhuo Wang, Fan Qin, Quan Pan, Chang Liu, Hongjia Zhu, Wenbo Li, Hongmei Yan, Wei Huang

{"title":"Enhancing Neural Encoding of Natural Scenes through Hierarchical Integration of Saliency and Semantic Context.","authors":"Sizhuo Wang, Fan Qin, Quan Pan, Chang Liu, Hongjia Zhu, Wenbo Li, Hongmei Yan, Wei Huang","doi":"10.1142/S0129065727500201","DOIUrl":"https://doi.org/10.1142/S0129065727500201","url":null,"abstract":"Understanding how the human brain encodes complex natural scenes remains a central problem in computational neuroscience and artificial intelligence. Existing visual encoding models often rely on a single dominant feature representation and may insufficiently characterize how saliency-guided spatial information and high-level semantic context jointly contribute to cortical response prediction. To address this issue, this study proposes a saliency-guided multimodal visual encoding model, termed SMG-MVEM, to predict voxel-wise cortical responses to natural scene stimuli. The model integrates image features, saliency cues, and text-derived semantic representations through a hierarchical fusion architecture, followed by a Transformer-based brain mapper. Experiments on the Natural Scenes Dataset (NSD) show that SMG-MVEM improves prediction performance over representative neural encoding baselines and internal control variants, with the average PCC increasing from [Formula: see text] for the best-performing baseline to [Formula: see text]. Regional analyses further show that saliency contributed more strongly to early visual areas, whereas semantic features provided greater benefits in higher-order regions. Representational analyses also suggest that the model-predicted responses preserved aspects of hierarchical and category-related organization across the visual cortex. These findings indicate that structured integration of saliency and semantic context can improve cortical response prediction and provide interpretable representational patterns for natural vision.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750020"},"PeriodicalIF":6.4,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868491","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}

引用次数: 0

A Fair Energy Comparison of Spiking and Artificial Neural Networks on a General-Purpose Microcontroller. 一种通用微控制器上脉冲与人工神经网络的能量比较。

IF 6.4

International journal of neural systems Pub Date : 2026-08-27 DOI: 10.1142/S0129065727500092

Jonas Hansert, Franck Gechter, Peter Offermann

{"title":"A Fair Energy Comparison of Spiking and Artificial Neural Networks on a General-Purpose Microcontroller.","authors":"Jonas Hansert, Franck Gechter, Peter Offermann","doi":"10.1142/S0129065727500092","DOIUrl":"https://doi.org/10.1142/S0129065727500092","url":null,"abstract":"Energy efficiency is a key constraint for deploying machine learning models on resource-limited internet of things (IoT) and edge devices. Spiking Neural Networks (SNNs) are often proposed as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their event-driven computation paradigm. However, direct and fair energy comparisons between SNNs and ANNs on inexpensive general-purpose microcontrollers remain limited. In this work, we present a controlled experimental comparison of inference-time energy consumption between an SNN and an ANN with identical multilayer perceptron architectures deployed on an ESP32-C6 microcontroller. Both networks are implemented in C/C++ without relying on optimized inference libraries, use int8 quantization, and are evaluated on the MNIST dataset. Energy consumption is measured directly during inference under identical implementation and measurement conditions to isolate the impact of the computational paradigm. Quantitatively, the ANN requires 11.01 mJ per inference, whereas the SNN consumes 2.31 mJ, 2.34 mJ, and 3.88 mJ for 1, 3, and 10 simulation timesteps, respectively. This corresponds to an approximate reduction in energy consumption of about [Formula: see text] for a single timestep and still about [Formula: see text] even at ten timesteps, demonstrating that the SNN maintains a substantial efficiency advantage under the evaluated conditions. These findings indicate that spiking neural networks can provide significant inference-time energy savings on widely available microcontroller units even without specialized hardware acceleration, while emphasizing the importance of controlled and reproducible comparison conditions.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750009"},"PeriodicalIF":6.4,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148835748","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}

引用次数: 0

An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction. 用于构效关系预测的注意门控图脉冲神经膜系统。

IF 6.4

International journal of neural systems Pub Date : 2026-08-01 Epub Date: 2026-02-28 DOI: 10.1142/S0129065726500231

Jun Fu, Jianyi Zhang, Hong Peng, Wenxuan Zhang, Kaiying Han, Xiali Hei

{"title":"An Attention-Gated Graph Spiking Neural Membrane System for Structure-Activity Relationship Prediction.","authors":"Jun Fu, Jianyi Zhang, Hong Peng, Wenxuan Zhang, Kaiying Han, Xiali Hei","doi":"10.1142/S0129065726500231","DOIUrl":"10.1142/S0129065726500231","url":null,"abstract":"Spiking Neural P (SNP) systems have attracted increasing attention due to their biologically inspired, event-driven computation and inherent capability for temporal modeling. However, most existing SNP variants rely on fixed or purely local information propagation mechanisms, which limits their ability to capture long-range dependencies and contextual interactions in complex structured data. To address this limitation, we propose an Attention-Gated Spiking Neural membrane system (AGSNP), which incorporates an attention-guided gating mechanism directly into the spiking neuron dynamics. Unlike prior SNP models that treat attention as an external aggregation operation, AGSNP embeds attention signals into the nonlinear spiking update and memory regulation process. This design enables adaptive information propagation across distant structural components while preserving biologically inspired spiking behavior. To evaluate the effectiveness of the proposed architecture, AGSNP is instantiated within a graph-based learning framework and applied to Structure Activity Relationship (SAR) prediction. Experiments on three publicly available benchmark datasets demonstrate that AGSNP consistently outperforms representative baseline methods. Notably, under limited data availability and severe class imbalance, the proposed model achieves improvements of approximately 2.0-5.7% in AUC and related metrics on the Tox21 dataset, and 3.5-17.0% on the MUV dataset.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650023"},"PeriodicalIF":6.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313506","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}

引用次数: 0

Local-Contextual Feature Fusion Network Based on Nonlinear Spiking Neural Model for Semantic Segmentation of Remote Sensing Images. 基于非线性尖峰神经模型的局部-上下文特征融合网络遥感图像语义分割。

IF 6.4

International journal of neural systems Pub Date : 2026-08-01 Epub Date: 2026-03-24 DOI: 10.1142/S0129065726500292

Junhao Du, Hong Peng, Bing Li, Zhicai Liu

{"title":"Local-Contextual Feature Fusion Network Based on Nonlinear Spiking Neural Model for Semantic Segmentation of Remote Sensing Images.","authors":"Junhao Du, Hong Peng, Bing Li, Zhicai Liu","doi":"10.1142/S0129065726500292","DOIUrl":"10.1142/S0129065726500292","url":null,"abstract":"Semantic segmentation of remote sensing (RS) image is crucial to tasks such as geographic research, land monitoring and environmental protection. In recent years, deep learning models built with convolutional neural networks (CNNs) and Transformer structures have proven effective in semantic segmentation task of RS images. With the increase in resolution of RS images in complex scenes and the complexity of high-resolution urban images, each object has rich textures and edges, and the distribution of objects is extremely irregular. To resolve above challenges, a semantic segmentation network for RS images is proposed, which employs an encoder-decoder structure, where four ResNet-18 blocks act as encoders, and four specially designed local-contextual Transformer blocks form the decoder. In order to effectively utilize local contextual features, a channel attention-feature fusion module using a novel nonlinear spiking neuron model is designed to assist the decoder in better feature recovery. The experimental results demonstrate that the proposed method is feasible and effective for semantic segmentation of RS images. Specifically, the suboptimal 86.42% and optimal 82.25% mIoU are achieved on Potsdam and Vaihingen datasets, respectively, and the best 52.4% and the near-optimal 65.3% mIoU on the LoveDA and UAVid datasets, respectively, for the proposed model.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650029"},"PeriodicalIF":6.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505800","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}

引用次数: 0

Self-Adaptation Spiking Neural Membrane Systems with Neuromodulators. 具有神经调节剂的自适应脉冲神经膜系统。

IF 6.4

International journal of neural systems Pub Date : 2026-08-01 Epub Date: 2026-02-16 DOI: 10.1142/S0129065726500164

Tianlai Li, Zengzeng Hao, Qianqian Ren, Xiu Yin, Xiyu Liu, Jie Xue

{"title":"Self-Adaptation Spiking Neural Membrane Systems with Neuromodulators.","authors":"Tianlai Li, Zengzeng Hao, Qianqian Ren, Xiu Yin, Xiyu Liu, Jie Xue","doi":"10.1142/S0129065726500164","DOIUrl":"10.1142/S0129065726500164","url":null,"abstract":"Spiking neural P systems (SN PS) exemplify the third-generation of spiking neural networks (SNNs), which perform distributed and concurrent computations. However, SN PS rely solely on the spike as the singular signaling entity, ignoring the influence of other substances in the biological nervous system. Neuromodulators have the ability to exert their effects on the postsynaptic membrane, influencing synaptic plasticity and modifying the intensity of interneuronal connections. Motivated by this biological observation, we introduce a self-adaptation spiking neural P system with neuromodulators (SSNN PS). Specifically, neuromodulators generated by neurons are designed as resources consumed by rules in the postsynaptic membrane. The postsynaptic membrane, functioning as a new computational unit with three novel rules, possesses self-adapting weights regulated by neuromodulators and reflects the intensity of connections between neurons. Therefore, the SSNN PS enhance the control of the system over the computing process. In this work, we demonstrate the Turing universality of SSNN PS as both a number-generating and a number-accepting device. In addition, to verify the application capability, an SSNN PS for gender recognition of face images was constructed. The postsynaptic membrane self-adaptively updates its weights as it receives the feedback neuromodulators, which makes the recognition result more accurate. It achieves an accuracy of 91.71% on the UTKFace dataset and 87.83% on the FairFace dataset, and outperforms the other five comparative methods.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650016"},"PeriodicalIF":6.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204512","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}

引用次数: 0

Causal Mask in Transformer via Transfer Entropy Estimation from Vector Autoregressive Learning for Multivariate Time Series Forecasting. 基于矢量自回归学习的多变量时间序列预测传递熵估计的变压器因果掩模。

IF 6.4

International journal of neural systems Pub Date : 2026-08-01 Epub Date: 2026-03-19 DOI: 10.1142/S012906572650019X

Chengli Zhou, Zicheng Wang, Yaqun Huang, Dapeng Tao, Chunna Zhao

引用次数: 0