International journal of neural systems最新文献_第3页

Neural Memory State Space Models for Medical Image Segmentation. 用于医学图像分割的神经记忆状态空间模型

International journal of neural systems Pub Date : 2025-01-01 Epub Date: 2024-09-30 DOI: 10.1142/S0129065724500680

Zhihua Wang, Jingjun Gu, Wang Zhou, Quansong He, Tianli Zhao, Jialong Guo, Li Lu, Tao He, Jiajun Bu

{"title":"Neural Memory State Space Models for Medical Image Segmentation.","authors":"Zhihua Wang, Jingjun Gu, Wang Zhou, Quansong He, Tianli Zhao, Jialong Guo, Li Lu, Tao He, Jiajun Bu","doi":"10.1142/S0129065724500680","DOIUrl":"10.1142/S0129065724500680","url":null,"abstract":"With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explored. One popular approach is incorporating transformers, though their quadratic computational complexity poses challenges. Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this paper, we explore the respective strengths and weaknesses of nmODEs and SSMs and propose a novel architecture, the nmSSM decoder, which combines the advantages of both approaches. This architecture possesses powerful nonlinear representation capabilities while retaining the ability to preserve input and process global information. We construct nmSSM-UNet using the nmSSM decoder and conduct comprehensive experiments on the PH2, ISIC2018, and BU-COCO datasets to validate its effectiveness in medical image segmentation. The results demonstrate the promising application value of nmSSM-UNet. Additionally, we conducted ablation experiments to verify the effectiveness of our proposed improvements on SSMs and nmODEs.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450068"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335214","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

Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity. 从皮层尖峰活动解码连续跟踪眼球运动

International journal of neural systems Pub Date : 2025-01-01 Epub Date: 2024-11-15 DOI: 10.1142/S0129065724500709

Kendra K Noneman, J Patrick Mayo

{"title":"Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.","authors":"Kendra K Noneman, J Patrick Mayo","doi":"10.1142/S0129065724500709","DOIUrl":"10.1142/S0129065724500709","url":null,"abstract":"Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450070"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640247","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

Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity. 基于脑电图功能连接性的阵发性运动障碍深度学习识别。

International journal of neural systems Pub Date : 2025-01-01 Epub Date: 2024-11-19 DOI: 10.1142/S0129065725500017

Liang Zhao, Renling Zou, Linpeng Jin

{"title":"Deep Learning Recognition of Paroxysmal Kinesigenic Dyskinesia Based on EEG Functional Connectivity.","authors":"Liang Zhao, Renling Zou, Linpeng Jin","doi":"10.1142/S0129065725500017","DOIUrl":"10.1142/S0129065725500017","url":null,"abstract":"Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic screening, face challenges in identifying secondary cases due to symptom overlap with other disorders. This study introduces a novel PKD recognition method utilizing a resting-state electroencephalogram (EEG) functional connectivity matrix and a deep learning architecture (AT-1CBL). Resting-state EEG data from 44 PKD patients and 44 healthy controls (HCs) were collected using a 128-channel EEG system. Functional connectivity matrices were computed and transformed into graph data to examine brain network property differences between PKD patients and controls through graph theory. Source localization was conducted to explore neural circuit differences in patients. The AT-1CBL model, integrating 1D-CNN and Bi-LSTM with attentional mechanisms, achieved a classification accuracy of 93.77% on phase lag index (PLI) features in the Theta band. Graph theoretic analysis revealed significant phase synchronization impairments in the Theta band of the functional brain network in PKD patients, particularly in the distribution of weak connections compared to HCs. Source localization analyses indicated greater differences in functional connectivity in sensorimotor regions and the frontal-limbic system in PKD patients, suggesting abnormalities in motor integration related to clinical symptoms. This study highlights the potential of deep learning models based on EEG functional connectivity for accurate and cost-effective PKD diagnosis, supporting the development of portable EEG devices for clinical monitoring and diagnosis. However, the limited dataset size may affect generalizability, and further exploration of multimodal data integration and advanced deep learning architectures is necessary to enhance the robustness of PKD diagnostic models.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550001"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670040","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