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Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-24 Kyungrak Choi, Yoonsuck Choe, Hangue Park
Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm
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Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-17 Jun Fu, Hong Peng, Bing Li, Zhicai Liu, Rikong Lugu, Jun Wang, Antonio Ramírez-de-Arellano
Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive
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Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-13 Haonan Pei, Sisi Jiang, Mei Liu, Guofeng Ye, Yun Qin, Yayun Liu, Mingjun Duan, Dezhong Yao, Cheng Luo
Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional
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A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-13 Chenglan Zhong, Yutao Xiong, Wei Tang, Jixiang Guo
Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral–maxillofacial surgery. Currently, the mainly available
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Bridges Between Spiking Neural Membrane Systems and Virus Machines Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-13 Antonio Ramírez-de-Arellano, David Orellana-Martín, Mario J. Pérez-Jiménez
Spiking Neural P Systems (SNP) are well-established computing models that take inspiration from spikes between biological neurons; these models have been widely used for both theoretical studies and practical applications. Virus machines (VMs) are an emerging computing paradigm inspired by viral transmission and replication. In this work, a novel extension of VMs inspired by SNPs is presented, called
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Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-13 Xingyang Liu, Haina Rong, Ferrante Neri, Zhangguo Yu, Gexiang Zhang
The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a novel approach aimed at efficiently optimizing robot controller parameters to enhance its motion performance. While spiking neural P systems have shown great
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Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-13 Zhengyuan Jiang, Siyan Sun, Hong Peng, Zhicai Liu, Jun Wang
Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector’s complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because
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Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer’s Disease Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-04-05 Noushath Shaffi, Karthikeyan Subramanian, Viswan Vimbi, Faizal Hajamohideen, Abdelhamid Abdesselam, Mufti Mahmud
Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer’s disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive
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Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-27 Danilo Avola, Luigi Cinque, Angelo Di Mambro, Alessio Fagioli, Marco Raoul Marini, Daniele Pannone, Bruno Fanini, Gian Luca Foresti
Emotion recognition plays an essential role in human–human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human–computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application
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Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-22 Lorenzo Rovida, Alberto Leporati
Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE)
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Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-21 Sriram Kumar P, Jac Fredo Agastinose Ronickom
In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and
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An Asynchronous Spiking Neural Membrane System for Edge Detection Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-16 Luping Zhang, Fei Xu, Ferrante Neri
Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the
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Edge Computing Transformers for Fall Detection in Older Adults Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-16 Jesús Fernandez-Bermejo, Jesús Martinez-del-Rincon, Javier Dorado, Xavier del Toro, María J. Santofimia, Juan C. Lopez
The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication
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A Parallel Convolutional Network Based on Spiking Neural Systems Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-15 Chi Zhou, Lulin Ye, Hong Peng, Zhicai Liu, Jun Wang, Antonio Ramírez-De-Arellano
Deep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) systems. Then, a U-shaped convolutional neural network named SNP-like parallel-convolutional network, or SPC-Net, is constructed for segmentation tasks
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Modular Spiking Neural Membrane Systems for Image Classification Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-03-08 Iris Ermini, Claudio Zandron
A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of bio-inspired machine learning algorithms. This paper proposes Modular Spiking Neural P (MSNP) systems to solve image classification problems, a novel SNP
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Robust Federated Learning for Heterogeneous Model and Data Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-28 Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti
Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals
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Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-28 Lin Chen, Lu Leng, Ziyuan Yang, Andrew Beng Jin Teoh
This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation
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Multimodal Covariance Network Reflects Individual Cognitive Flexibility Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-17 Lin Jiang, Simon B. Eickhoff, Sarah Genon, Guangying Wang, Chanlin Yi, Runyang He, Xunan Huang, Dezhong Yao, Debo Dong, Fali Li, Peng Xu
Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain’s structural–functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive
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Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-17 Sisi Jiang, Haonan Pei, Junxia Chen, Hechun Li, Zetao Liu, Yuehan Wang, Jinnan Gong, Sheng Wang, Qifu Li, Mingjun Duan, Vince D. Calhoun, Dezhong Yao, Cheng Luo
Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity
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Multi-Semantic Decoding of Visual Perception with Graph Neural Networks Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-17 Rong Li, Jiyi Li, Chong Wang, Haoxiang Liu, Tao Liu, Xuyang Wang, Ting Zou, Wei Huang, Hongmei Yan, Huafu Chen
Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate
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A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation Int. J. Neural Syst. (IF 8.0) Pub Date : 2024-02-06 Hao Niu, Zhang Yi, Tao He
Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing
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Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-12-01 Fangzhou Xu,Yihao Yan,Jianqun Zhu,Xinyi Chen,Licai Gao,Yanbing Liu,Weiyou Shi,Yitai Lou,Wei Wang,Jiancai Leng,Yang Zhang
Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC)
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Discriminative Power of Handwriting and Drawing Features in Depression. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-11-24 Claudia Greco,Gennaro Raimo,Terry Amorese,Marialucia Cuciniello,Gavin Mcconvey,Gennaro Cordasco,Marcos Faundez-Zanuy,Alessandro Vinciarelli,Zoraida Callejas-Carrion,Anna Esposito
This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric
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Neonatal White Matter Damage Analysis Using DTI Super-Resolution and Multi-Modality Image Registration. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-11-17 Yi Wang,Yuan Zhang,Chi Ma,Rui Wang,Zhe Guo,Yu Shen,Miaomiao Wang,Hongying Meng
Punctate White Matter Damage (PWMD) is a common neonatal brain disease, which can easily cause neurological disorder and strongly affect life quality in terms of neuromotor and cognitive performance. Especially, at the neonatal stage, the best cure time can be easily missed because PWMD is not conducive to the diagnosis based on current existing methods. The lesion of PWMD is relatively straightforward
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An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-11-15 Xinyuan Chen,Yi Niu,Yanna Zhao,Xue Qin
To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which
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Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-11-01 Shuangyong Zhang,Hong Wang,Zixi Zheng,Tianyu Liu,Weixin Li,Zishan Zhang,Yanshen Sun
Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels
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Hybrid Network for Patient-Specific Seizure Prediction from EEG Data. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-11-01 Yongfeng Zhang,Tiantian Xiao,Ziwei Wang,Hongbin Lv,Shuai Wang,Hailing Feng,Shanshan Zhao,Yanna Zhao
Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features
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A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-20 Enrique Adrian Villarrubia-Martin,Luis Rodriguez-Benitez,Luis Jimenez-Linares,David Muñoz-Valero,Jun Liu
Reinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers
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Lightweight Seizure Detection Based on Multi-Scale Channel Attention. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-17 Ziwei Wang,Sujuan Hou,Tiantian Xiao,Yongfeng Zhang,Hongbin Lv,Jiacheng Li,Shanshan Zhao,Yanna Zhao
Epilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important
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Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-13 Mosab A A Yousif,Mahmut Ozturk
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the
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Eye State Detection Using Frequency Features from 1 or 2-Channel EEG. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-12 Francisco Laport,Adriana Dapena,Paula M Castro,Daniel I Iglesias,Francisco J Vazquez-Araujo
Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels
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Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-07 Mariantonia Cotronei,Sofia Giuffrè,Attilio Marcianò,Domenico Rosaci,Giuseppe M L Sarnè
The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered
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An Integrated Neurorobotics Model of the Cerebellar-Basal Ganglia Circuitry. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-04 Jhielson M Pimentel,Renan C Moioli,Mariana F P De Araujo,Patricia A Vargas
This work presents a neurorobotics model of the brain that integrates the cerebellum and the basal ganglia regions to coordinate movements in a humanoid robot. This cerebellar-basal ganglia circuitry is well known for its relevance to the motor control used by most mammals. Other computational models have been designed for similar applications in the robotics field. However, most of them completely
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Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-10-01 Miguel A Vicente-Querol,Antonio Fernández-Caballero,Pascual González,Luz M González-Gualda,Patricia Fernández-Sotos,José P Molina,Arturo S García
Facial affect recognition is a critical skill in human interactions that is often impaired in psychiatric disorders. To address this challenge, tests have been developed to measure and train this skill. Recently, virtual human (VH) and virtual reality (VR) technologies have emerged as novel tools for this purpose. This study investigates the unique contributions of different factors in the communication
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Human Gait Activity Recognition Using Multimodal Sensors. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-09-30 Diego Teran-Pineda,Karl Thurnhofer-Hemsi,Enrique Domínguez
Human activity recognition is an application of machine learning with the aim of identifying activities from the gathered activity raw data acquired by different sensors. In medicine, human gait is commonly analyzed by doctors to detect abnormalities and determine possible treatments for the patient. Monitoring the patient's activity is paramount in evaluating the treatment's evolution. This type of
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Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-09-29 Jiaqi Cui,Jianghong Xiao,Yun Hou,Xi Wu,Jiliu Zhou,Xingchen Peng,Yan Wang
Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time
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Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-09-23 Junjie Hu,Chengrong Yu,Zhang Yi,Haixian Zhang
Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test
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Epileptic Seizure Prediction Using Attention Augmented Convolutional Network. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-09-07 Dongsheng Liu,Xingchen Dong,Dong Bian,Weidong Zhou
Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network
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Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task. Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-24 Han-Lin Wang,Yun-Ting Kuo,Yu-Chun Lo,Chao-Hung Kuo,Bo-Wei Chen,Ching-Fu Wang,Zu-Yu Wu,Chi-En Lee,Shih-Hung Yang,Sheng-Huang Lin,Po-Chuan Chen,You-Yin Chen
Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting
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Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-17 Sanaullah, Shamini Koravuna, Ulrich Rückert, Thorsten Jungeblut
Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality by building neurons and synapses that mimic the human brain’s transmission of electrical signals. However, optimal SNN implementation requires a precise balance of parametric values. To design such ubiquitous neural networks, a graphical tool for visualizing, analyzing, and explaining the internal behavior of spikes
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A Deep Regression Approach for Human Activity Recognition Under Partial Occlusion Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-19 Ioannis Vernikos, Evaggelos Spyrou, Ioannis-Aris Kostis, Eirini Mathe, Phivos Mylonas
In real-life scenarios, Human Activity Recognition (HAR) from video data is prone to occlusion of one or more body parts of the human subjects involved. Although it is common sense that the recognition of the majority of activities strongly depends on the motion of some body parts, which when occluded compromise the performance of recognition approaches, this problem is often underestimated in contemporary
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Decoupled Edge Guidance Network for Automatic Checkout Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-10 Rongbiao You, Fuxiong He, Weiming Lin
Automatic checkout (ACO) aims at correctly generating complete shopping lists from checkout images. However, the domain gap between the single product in training data and multiple products in checkout images endows ACO tasks with a major difficulty. Despite remarkable advancements in recent years, resolving the significant domain gap remains challenging. It is possibly because networks trained solely
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Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-10 Axel De Nardin, Silvia Zottin, Claudio Piciarelli, Emanuela Colombi, Gian Luca Foresti
Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many
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Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-09 Sepehr Shirani, Antonio Valentin, Bahman Abdi-Sargezeh, Gonzalo Alarcon, Saeid Sanei
Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer
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Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time–Frequency Features of EEG Data Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-08-02 Ozlem Karabiber Cura, Aydin Akan, Hatice Sabiha Ture
The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG
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Online Ternary Classification of Covert Speech by Leveraging the Passive Perception of Speech Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-07-31 Jae Moon, Tom Chau
Brain–computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally
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A Class-Imbalance Aware and Explainable Spatio-Temporal Graph Attention Network for Neonatal Seizure Detection Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-07-28 Khadijeh Raeisi, Mohammad Khazaei, Gabriella Tamburro, Pierpaolo Croce, Silvia Comani, Filippo Zappasodi
Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the
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Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson’s Disease Using Multimodal Data Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-07-21 D. Castillo-Barnes, F. J. Martinez-Murcia, C. Jimenez-Mesa, J. E. Arco, D. Salas-Gonzalez, J. Ramírez, J. M. Górriz
Parkinson’s Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional
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A Transformer-Embedded Multi-Task Model for Dose Distribution Prediction Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-07-07 Lu Wen, Jianghong Xiao, Shuai Tan, Xi Wu, Jiliu Zhou, Xingchen Peng, Yan Wang
Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on experience, causing it extremely subjective and time-consuming to obtain a clinically acceptable plan. To this end, we introduce a transformer-embedded multi-task dose prediction (TransMTDP) network to automatically
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Epileptic EEG Classification via Graph Transformer Network Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-30 Jian Lian, Fangzhou Xu
Deep learning-based epileptic seizure recognition via electroencephalogram signals has shown considerable potential for clinical practice. Although deep learning algorithms can enhance epilepsy identification accuracy compared with classical machine learning techniques, classifying epileptic activities based on the association between multichannel signals in electroencephalogram recordings is still
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Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding: A Second-Order Multi-Adaptive Neural Agent Model Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-28 Sophie C. F. Hendrikse, Jan Treur, Sander L. Koole
When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent
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Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-17 Niki Kiriakidou, Christos Diou
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effects. However, these data suffer from several weaknesses, leading to inaccurate causal effect estimations, if not handled properly. Therefore, several machine
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Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-17 Florin Macicasan, Alexandru Frasie, Nicoleta-Teodora Vezan, Camelia Lemnaru, Rodica Potolea
Abstract Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text. This paper extends an existing 2-stage pipeline AMR parser with state-of-the-art techniques in dependency parsing. First, Pointer-Generator Networks are used for out-of-vocabulary words in the concept identification stage, with an improved
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A Shared Hippocampal Network in Retrieving Science-related Semantic Memories Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-15 Hsiao-Ching She, Li-Yu Huang, Jeng-Ren Duann
In responding to the calls for revisiting the role that hippocampus (HIP) plays in semantic memory retrieval, this study used functional neuroimaging-based connectivity technique to elucidate the functional brain network involved in retrieving the correct and incorrect science-related semantic memories. Unlike episodic memory retrieval, the 40 scientific concepts learned during middle and high school
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Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-14 Wei Liu, Hui Chen, Yongqiang Ma, Jianji Wang, Nanning Zheng
Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detection of unseen classes by aligning object region proposals with semantic embeddings. However, these methods have several limitations, including poor region
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An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-10 Qian Liu, Yanping Huang, Qian Yang, Hong Peng, Jun Wang
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with
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A Modified Long Short-Term Memory Cell Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-06-09 Giannis Haralabopoulos, Gerasimos Razis, Ioannis Anagnostopoulos
Machine Learning (ML), among other things, facilitates Text Classification, the task of assigning classes to textual items. Classification performance in ML has been significantly improved due to recent developments, including the rise of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Transformer Models. Internal memory states with dynamic temporal
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Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-05-27 Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti
Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem.
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Facial Expression Recognition with Contrastive Learning and Uncertainty-Guided Relabeling Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-05-16 Yujie Yang, Lin Hu, Chen Zu, Qizheng Zhou, Xi Wu, Jiliu Zhou, Yan Wang
Facial expression recognition (FER) plays a vital role in the field of human-computer interaction. To achieve automatic FER, various approaches based on deep learning (DL) have been presented. However, most of them lack for the extraction of discriminative expression semantic information and suffer from the problem of annotation ambiguity. In this paper, we propose an elaborately designed end-to-end
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One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain Int. J. Neural Syst. (IF 8.0) Pub Date : 2023-05-15 Fangzhou Xu, Chongfeng Wang, Xin Yu, Jinzhao Zhao, Ming Liu, Jiaqi Zhao, Licai Gao, Xiuquan Jiang, Zhaoxin Zhu, Yongjian Wu, Dezheng Wang, Shanxin Feng, Sen Yin, Yang Zhang, Jiancai Leng
Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional