International journal of neural systems最新文献_第2页

Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios. 探索脉冲神经网络的多功能性：跨不同场景的应用。

International journal of neural systems Pub Date : 2024-12-23 DOI: 10.1142/S0129065725500078

Matteo Cavaleri, Claudio Zandron

{"title":"Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios.","authors":"Matteo Cavaleri, Claudio Zandron","doi":"10.1142/S0129065725500078","DOIUrl":"https://doi.org/10.1142/S0129065725500078","url":null,"abstract":"In the last few decades, Artificial Neural Networks have become more and more important, evolving into a powerful tool to implement learning algorithms. Spiking neural networks represent the third generation of Artificial Neural Networks; they have earned growing significance due to their remarkable achievements in pattern recognition, finding extensive utility across diverse domains such as e.g. diagnostic medicine. Usually, Spiking Neural Networks are slightly less accurate than other Artificial Neural Networks, but they require a reduced amount of energy to perform calculations; this amount of energy further reduces in a very significant manner if they are implemented on hardware specifically designed for them, like neuromorphic hardware. In this work, we focus on exploring the versatility of Spiking Neural Networks and their potential applications across a range of scenarios by exploiting their adaptability and dynamic processing capabilities, which make them suitable for various tasks. A first rough network is designed based on the dataset's general attributes; the network is then refined through an extensive grid search algorithm to identify the optimal values for hyperparameters. This dual-step process ensures that the Spiking Neural Network can be tailored to diverse and potentially very different situations in a direct and intuitive manner. We test this by considering three different scenarios: epileptic seizure detection, both considering binary and multi-classification tasks, as well as wine classification. The proposed methodology turned out to be highly effective in binary class scenarios: the Spiking Neural Networks models achieved significantly lower energy consumption compared to Artificial Neural Networks while approaching nearly 100% accuracy. In the case of multi-class classification, the model achieved an accuracy of approximately 90%, thus indicating that it can still be further improved.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550007"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879122","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 Cloud Detection Network Based on Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net. 基于自适应拉普拉斯协调增强型交叉特征 U-Net 的云检测网络。

International journal of neural systems Pub Date : 2024-12-13 DOI: 10.1142/S0129065725500054

Kaizheng Wang, Ruohan Zhou, Jian Wang, Ferrante Neri, Yitong Fu, Shunzhen Zhou

{"title":"A Cloud Detection Network Based on Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net.","authors":"Kaizheng Wang, Ruohan Zhou, Jian Wang, Ferrante Neri, Yitong Fu, Shunzhen Zhou","doi":"10.1142/S0129065725500054","DOIUrl":"https://doi.org/10.1142/S0129065725500054","url":null,"abstract":"Cloud cover experiences rapid fluctuations, significantly impacting the irradiance reaching the ground and causing frequent variations in photovoltaic power output. Accurate detection of thin and fragmented clouds is crucial for reliable photovoltaic power generation forecasting. In this paper, we introduce a novel cloud detection method, termed Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net (ALCU-Net). This method augments the traditional U-Net architecture with three innovative components: an Adaptive Feature Coordination (AFC) module, an Adaptive Laplacian Cross-Feature U-Net with a Multi-Grained Laplacian-Enhanced (MLE) feature module, and a Criss-Cross Feature Fused Detection (CCFE) module. The AFC module enhances spatial coherence and bridges semantic gaps across multi-channel images. The Adaptive Laplacian Cross-Feature U-Net integrates features from adjacent hierarchical levels, using the MLE module to refine cloud characteristics and edge details over time. The CCFE module, embedded in the U-Net decoder, leverages criss-cross features to improve detection accuracy. Experimental evaluations show that ALCU-Net consistently outperforms existing cloud detection methods, demonstrating superior accuracy in identifying both thick and thin clouds and in mapping fragmented cloud patches across various environments, including oceans, polar regions, and complex ocean-land mixtures.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550005"},"PeriodicalIF":0.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824883","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

Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation. 利用神经记忆常微分方程精确定位颈椎的解剖生理特征

International journal of neural systems Pub Date : 2024-12-01 Epub Date: 2024-07-25 DOI: 10.1142/S0129065724500564

Xi Zheng, Yi Yang, Dehan Li, Yi Deng, Yuexiong Xie, Zhang Yi, Litai Ma, Lei Xu

{"title":"Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation.","authors":"Xi Zheng, Yi Yang, Dehan Li, Yi Deng, Yuexiong Xie, Zhang Yi, Litai Ma, Lei Xu","doi":"10.1142/S0129065724500564","DOIUrl":"10.1142/S0129065724500564","url":null,"abstract":"In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450056"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763528","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

The 2024 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. 公告：2024 年霍贾特-阿德利神经系统杰出贡献奖。

International journal of neural systems Pub Date : 2024-12-01 Epub Date: 2024-09-23 DOI: 10.1142/S012906572482001X

Han Sun

引用次数: 0

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection. 用于高效癫痫发作检测的轻量级卷积神经网络-重构器模型

International journal of neural systems Pub Date : 2024-12-01 Epub Date: 2024-09-30 DOI: 10.1142/S0129065724500655

Haozhou Cui, Xiangwen Zhong, Haotian Li, Chuanyu Li, Xingchen Dong, Dezan Ji, Landi He, Weidong Zhou

{"title":"A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.","authors":"Haozhou Cui, Xiangwen Zhong, Haotian Li, Chuanyu Li, Xingchen Dong, Dezan Ji, Landi He, Weidong Zhou","doi":"10.1142/S0129065724500655","DOIUrl":"10.1142/S0129065724500655","url":null,"abstract":"A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1[Formula: see text]h of multi-channel EEG signals is 1.92[Formula: see text]s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450065"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335213","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

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems. 基于卷积非线性尖峰神经膜系统的多模态特征交互和对齐的参考图像分割。

International journal of neural systems Pub Date : 2024-12-01 Epub Date: 2024-09-23 DOI: 10.1142/S0129065724500643

Siyan Sun, Peng Wang, Hong Peng, Zhicai Liu

引用次数: 0

Sparse Spike Feature Learning to Recognize Traceable Interictal Epileptiform Spikes. 稀疏尖峰特征学习识别可追踪的癫痫发作间期尖峰。

International journal of neural systems Pub Date : 2024-11-30 DOI: 10.1142/S0129065724500710

Chenchen Cheng, Yunbo Shi, Yan Liu, Bo You, Yuanfeng Zhou, Ardalan Aarabi, Yakang Dai

{"title":"Sparse Spike Feature Learning to Recognize Traceable Interictal Epileptiform Spikes.","authors":"Chenchen Cheng, Yunbo Shi, Yan Liu, Bo You, Yuanfeng Zhou, Ardalan Aarabi, Yakang Dai","doi":"10.1142/S0129065724500710","DOIUrl":"https://doi.org/10.1142/S0129065724500710","url":null,"abstract":"Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated. However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy neurosurgery. Therefore, identifying spike subtypes that are strongly associated with epileptogenic focus (traceable spikes) could facilitate their use as reliable signal sources for accurately tracing epileptogenic focus. However, the sparse firing phenomenon in the transmission of intracranial neuronal discharges leads to differences within spikes that cannot be observed visually. Therefore, neuro-electro-physiologists are unable to identify traceable spikes that could accurately locate epileptogenic focus. Herein, we propose a novel sparse spike feature learning method to recognize traceable spikes and extract discrimination information related to epileptogenic focus. First, a multilevel eigensystem feature representation was determined based on a multilevel feature representation module to express the intrinsic properties of a spike. Second, the sparse feature learning module expressed the sparse spike multi-domain context feature representation to extract sparse spike feature representations. Among them, a sparse spike encoding strategy was implemented to effectively simulate the sparse firing phenomenon for the accurate encoding of the activity of intracranial neurosources. The sensitivity of the proposed method was 97.1%, demonstrating its effectiveness and significant efficiency relative to other state-of-the-art methods.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450071"},"PeriodicalIF":0.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756066","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

Anomaly Detection Using Complete Cycle Consistent Generative Adversarial Network. 基于完全循环一致生成对抗网络的异常检测。

International journal of neural systems Pub Date : 2024-11-30 DOI: 10.1142/S0129065725500042

Zahra Dehghanian, Saeed Saravani, Maryam Amirmazlaghani, Mohamad Rahmati

{"title":"Anomaly Detection Using Complete Cycle Consistent Generative Adversarial Network.","authors":"Zahra Dehghanian, Saeed Saravani, Maryam Amirmazlaghani, Mohamad Rahmati","doi":"10.1142/S0129065725500042","DOIUrl":"https://doi.org/10.1142/S0129065725500042","url":null,"abstract":"This research presents a robust adversarial method for anomaly detection in real-world scenarios, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Traditional approaches often falter due to high variance in class-wise accuracy, rendering them ineffective across different anomaly types. Our proposed model addresses these challenges by introducing an innovative flow of information in the training procedure and integrating it as a new discriminator into the framework, thereby optimizing the training dynamics. Furthermore, it employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution. This adjustment distinctly isolates anomalous instances and enhances detection precision. Also, two unique anomaly scoring mechanisms were developed to augment detection capabilities. Comprehensive evaluations on six varied datasets have confirmed that our model outperforms one-class anomaly detection benchmarks. The implementation is openly accessible to the academic community, available on Github.a.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550004"},"PeriodicalIF":0.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776000","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

SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition. SATEER：基于脑电图的情感识别主体感知变换器。

International journal of neural systems Pub Date : 2024-11-20 DOI: 10.1142/S0129065725500029

Romeo Lanzino, Danilo Avola, Federico Fontana, Luigi Cinque, Francesco Scarcello, Gian Luca Foresti

{"title":"SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition.","authors":"Romeo Lanzino, Danilo Avola, Federico Fontana, Luigi Cinque, Francesco Scarcello, Gian Luca Foresti","doi":"10.1142/S0129065725500029","DOIUrl":"10.1142/S0129065725500029","url":null,"abstract":"This study presents a Subject-Aware Transformer-based neural network designed for the Electroencephalogram (EEG) Emotion Recognition task (SATEER), which entails the analysis of EEG signals to classify and interpret human emotional states. SATEER processes the EEG waveforms by transforming them into Mel spectrograms, which can be seen as particular cases of images with the number of channels equal to the number of electrodes used during the recording process; this type of data can thus be processed using a Computer Vision pipeline. Distinct from preceding approaches, this model addresses the variability in individual responses to identical stimuli by incorporating a User Embedder module. This module enables the association of individual profiles with their EEGs, thereby enhancing classification accuracy. The efficacy of the model was rigorously evaluated using four publicly available datasets, demonstrating superior performance over existing methods in all conducted benchmarks. For instance, on the AMIGOS dataset (A dataset for Multimodal research of affect, personality traits, and mood on Individuals and GrOupS), SATEER's accuracy exceeds 99.8% accuracy across all labels and showcases an improvement of 0.47% over the state of the art. Furthermore, an exhaustive ablation study underscores the pivotal role of the User Embedder module and each other component of the presented model in achieving these advancements.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550002"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670049","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 Modified Transformer Network for Seizure Detection Using EEG Signals. 利用脑电信号检测癫痫发作的改良变压器网络

International journal of neural systems Pub Date : 2024-11-19 DOI: 10.1142/S0129065725500030

Wenrong Hu, Juan Wang, Feng Li, Daohui Ge, Yuxia Wang, Qingwei Jia, Shasha Yuan

{"title":"A Modified Transformer Network for Seizure Detection Using EEG Signals.","authors":"Wenrong Hu, Juan Wang, Feng Li, Daohui Ge, Yuxia Wang, Qingwei Jia, Shasha Yuan","doi":"10.1142/S0129065725500030","DOIUrl":"10.1142/S0129065725500030","url":null,"abstract":"Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550003"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670034","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