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Computational model investigating the effect of magnetic field on neural–astrocyte microcircuit

Chinese Physics B 2021年第6期30卷 711-720页

作者： Li-Cong Li Jin Zhou Hong-Ji Sun Peng Xiong Hong-Rui Wang Xiu-Ling Liu Chang-Yong Wang College of Physics Science and Technology Hebei UniversityBaoding 071002China Key Laboratory of Digital Medical Engineering of Hebei Province Hebei UniversityBaoding 071002China Department of Neural Engineering and Biological Interdisciplinary Studies Institute of Military Cognition and Brain SciencesAcademy of Military Medical SciencesBeijing 100850China Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China

Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that "non-neuronal" cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation(TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its *** model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents(SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.

关键词： magnetic stimulation neural–astrocyte microcircuit excitatory and inhibitory balance computational model

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CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation

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Cyborg and Bionic Systems 2023年第1期4卷 318-329页

作者： Mengfan Li Ran Wei Ziqi Zhang Pengfei Zhang Guizhi Xu Wenzhe Liao State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical EngineeringHebei University of Technology300132 TianjinChina Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering 300132 TianjinChina Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health 300132 TianjinChina School of Artificial Intelligence Hebei University of Technology300132 TianjinChina

Brain-computer interface(BCI)is a typical direction of integration of human intelligence and robot *** control is an essential form of combining human and robot agents in a common task,but still faces a lack of freedom for the human *** paper proposes a Centroidal Voronoi Tessellation(CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous *** electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control.A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal *** event-related potential of the BCI is designed for target selection to communicate with the *** robot has an autonomous navigation function to reach the human selected goals.A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous(CVT-A)BCI *** subjects participated in the experiment,and they were instructed to control the robot to navigate toward a destination with obstacle avoidance *** results show that the CVT-A BCI system can shorten the task duration,decrease the command times,and optimize navigation path,compared with the single-step ***,this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.

关键词： robot agent Robot

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A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism

arXiv

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arXiv 2024年

作者： Zheng, Peng Zhou, Qian State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Hebei University of Technology Tianjin300130 China Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering Hebei University of Technology Tianjin300130 China

Spiking neural Networks (SNNs) are well-suited for processing event streams from Dynamic Visual Sensors (DVSs) due to their use of sparse spike-based coding and asynchronous event-driven computation. To extract features from DVS objects, SNNs commonly use event-driven convolution with fixed kernel parameters. These filters respond strongly to features in specific orientations while disregarding others, leading to incomplete feature extraction. To improve the current event-driven convolution feature extraction capability of SNNs, we propose a DVS object recognition model that utilizes a trainable event-driven convolution and a spiking attention mechanism. The trainable event-driven convolution is proposed in this paper to update its convolution kernel through gradient descent. This method can extract local features of the event stream more efficiently than traditional event-driven convolution. Furthermore, the spiking attention mechanism is used to extract global dependence features. The classification performances of our model are better than the baseline methods on two neuromorphic datasets including MNIST-DVS and the more complex CIFAR10-DVS. Moreover, our model showed good classification ability for short event streams. It was shown that our model can improve the performance of event-driven convolutional SNNs for DVS objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Ship Power Sharing Strategy Based on Shapley Value Method and Cartel Mechanism

A Ship Power Sharing Strategy Based on Shapley Value Method ...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Zhang, Zhengxu Yang, Jie Ma, Kai Guo, Shiliang Li, Hui School of Electrical Engineering Yanshan University Qinhuangdao China Yanshan University Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education Qinhuangdao China

ISBN: (纸本)9798331504755

With the development of the maritime industry, the number and capacity of existing port cold-ironing (CI) equipment can no longer meet the power demand of ships during berthing, resulting in significant carbon emissions from ships within the port area. Therefore, a ship power sharing strategy based on Shapley value method and Cartel mechanism is proposed. Firstly, the rolling optimization model of ship power scheduling is established to adjust the ship power scheduling strategy according to the real-time information. Secondly, the cooperative game model of ship power sharing is established, and the cost sharing among cooperative ships is realized by Shapley value method. Finally, in order to reduce the situation that ships increase their own profits by deviating from cooperation and affect the overall profits, this paper introduces Cartel mechanism to punish the behavior of deviating from cooperation. The simulation results show that the power sharing strategy proposed in this paper can reduce the total ship cost and carbon emission by 5.28% and 3.78%, respectively. The introduction of Cartel mechanism can increase the cost of non-cooperative behavior, thus ensuring that all ships take cooperative behavior. The power sharing strategy based on Shapley method and Cartel mechanism proposed in this paper is helpful to the construction of green ports. © 2024 IEEE.

关键词： Profitability

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A Dynamic Vision Sensor Object Recognition Model Based on Trainable Event-Driven Convolution and Spiking Attention Mechanism

SSRN

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SSRN 2024年

关键词： Convolution

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Krüppel-like factor 7 attenuates hippocampal neuronal injury after traumatic brain injury

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neural Regeneration Research 2022年第3期17卷 661-672页

作者： Wen-Yuan Li Xiu-Mei Fu Zhen-Dong Wang Zhi-Gang Li Duo Ma Ping Sun Gui-Bo Liu Xiao-Feng Zhu Ying Wang Institute of Neural Tissue Engineering Mudanjiang Medical UniversityMudanjiangHeilongjiang ProvinceChina Department of Anatomy College of Basic Medical SciencesChengde Medical UniversityChengdeHebei ProvinceChina Hebei Key Laboratory of Nerve Injury and Repair Chengde Medical UniversityChengdeHebei ProvinceChina Department of Otorhinolaryngology Mudanjiang City Second People’s HospitalMudanjiangHeilongjiang ProvinceChina The First Department of General Surgery Hongqi HospitalMudanjiang Medical UniversityMudanjiangHeilongjiang ProvinceChina

Our previous study has shown that the transcription factor Krüppel-like factor 7(KLF7) promotes peripheral nerve regeneration and motor function recovery after spinal cord ***7 also participates in traumatic brain injury,but its regulatory mechanisms remain poorly *** the present study,an HT22 cell model of traumatic brain injury was established by stretch injury and oxygenglucose *** cells were then transfected with an adeno-associated virus carrying KLF7(AAV-KLF7).The results revealed that,after stretch injury and oxygen-glucose deprivation,KLF7 greatly reduced apoptosis,activated caspase-3 and lactate dehydrogenase,downregulated the expression of the apoptotic markers B-cell lymphoma 2(Bcl-2)-associated X protein(Bax) and cleaved caspase-3,and increased the expression of βIII-tubulin and the antiapoptotic marker ***,KLF7 overexpression upregulated Janus kinase 2(JAK2) and signal transducer and activator of transcription 3(STAT3) phosphorylation in HT22 cells treated by stretch injury and oxygenglucose *** assays revealed that KLF7 directly participated in the phosphorylation of *** addition,treatment with AG490,a selective inhibitor of JAK2/STAT3,weakened the protective effects of KLF7.A mouse controlled cortical impact model of traumatic brain injury was then *** 30 minutes before modeling,AAV-KLF7 was injected into the ipsilateral lateral *** protein and m RNA levels of KLF7 in the hippocampus were increased at 1 day after injury and recovered to normal levels at 3 days after ***7 reduced ipsilateral hippocampal atrophy,decreased the injured cortex volume,downregulated Bax and cleaved caspase-3 expression,and increased the number of 5-bromo-2'-deoxyuridine-positive neurons and Bcl-2 protein ***,KLF7 transfection greatly enhanced the phosphorylation of JAK2 and STAT3 in the ipsilateral *** results suggest that KLF7 may protect h

关键词： apoptosis hippocampus JAK2/STAT3 Kruppel-like factor 7 neuroprotection oxygen-glucose deprivation stretch traumatic brain injury

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Research on Brain-Controlled Robot System Based on Intelligent Decision Fusion Shared Control

Research on Brain-Controlled Robot System Based on Intellige...

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第43届中国控制会议

作者： Ziqi Zhang Mengfan Li Jingyu Wang Chunlong Wu State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Science and Biomedical EngineeringHebei University of Technology Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health School of Health Science and Biomedical Engineering Hebei University of Technology School of Mechanical Engineering Hebei University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

As machine intelligence continues to advance, the role of Human-Robot Collaboration（HRC） control technology becomes increasingly prominent. In current control systems, it is crucial to consider how to appropriately integrate human decision-making with robotic decision-making on the foundation of machine intelligence. This integration aims to fully leverage the respective decision-making strengths of humans and robots. Brain-Computer Interface（BCI）, as a technology that utilizes human biological signals to recognize human intentions, can accurately convey human decisions to the system. Addressing the issue of limited interaction in brain-controlled robot navigation in unstructured environments, this study proposes a navigation road segmentation method based on the U-net algorithm. This method extracts the effective navigation area and subdivides it,enabling the selection of navigation points *** robot, controlled based on the movease architecture, accomplishes automatic navigation to reach the user-selected navigation points. Through experiments comparing with the single-step shared control mode（SS-SC）, the effectiveness of the intelligent decision fusion shared control（IDF-SC） mode is validated. Eight participants took part in the experiment, where they were instructed to control the robot to navigate towards the endpoint and complete obstacle avoidance tasks. The experimental results indicate that the IDF-SC mode can shorten task duration, reduce the number of commands, and optimize navigation paths. The research outcomes of this project contribute to enhancing the safety and intelligence in the field of human-machine collaboration.

关键词： Human-robot collaboration Brain-computer interface U-net network shared control

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Decoding of Motor Imagery based on Multi-scale Residual Network and Multi-head Attention block

Decoding of Motor Imagery based on Multi-scale Residual Netw...

引用

第43届中国控制会议

作者： Jundi Li Mengfan Li Song Zhang Zhenghua Xu Guizhi Xu School of Health Science and Biomedical Engineering Hebei University of Technology Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical EngineeringHebei University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

One of the important parts in motion imagery brain-computer interfaces is EEG decoding, and deep learning has a huge potential to play a crucial role in the EEG end-to-end decoding process. Currently, the challenge for deep learning is to cope with time-varying EEG signals to be able to maintain robust decoding and keep high classification performance. This study proposes a multiscale residual network containing an attention mechanism(MSRANet) to decode four-classified motor *** MSRANet utilises a multi-head attentional mechanism to focus on the important information of EEG features;and applies a multiscale residual block to extract the enriched EEG features as well as employs a shared portion of the block parameters in order to extract the common features. Compared with the five baseline methods, MSRANet has the highest accuracy on BCI Competition 2a and 83.02% in the session-specific task. Thus, the proposed MSRANet can robustly decode time-varying EEG, save the cost of using BCI, and facilitate long-term use.

关键词： Brain-computer interface multi-scale residual network EEG decoding motor imagery

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Fabrication of Highly Aligned Mwcnts Loaded Ngcs for Peripheral Nerve Regeneration Via a Novel One-Step Modified Electrohydrodynamic Printing Method

SSRN

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SSRN 2023年

作者： Sun, Renyuan Lang, Yuna Wang, Baolin Zhao, Mingkang Li, Chao Liu, Shiheng Zhang, Longfei Chang, Ming-Wei State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Tianjin Key Laboratory of Bio-electromagnetic and Neural engineering Hebei University of Technology Tianjin300132 China Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300132 China Nanotechnology and Integrated Bioengineering Centre Jordanstown Campus University of Ulster NewtownabbeyBT37 0QB United Kingdom

The directional growth of Schwann cell can promote the repair of peripheral nerve injury (PNI) and aligned fibers have been proved to promote the directional growth of Schwann cell. However, the preparation of nerve guidance conduits (NGCs) composed of aligned fibers is still a challenge. The aim of this study was to develop a highly aligned NGCs composed of coaxial fibers via a novel one-step modified electrohydrodynamic (EHD) printing method. Multi-walled carbon nanotubes (MWCNTs) were incorporated into the polycaprolactone (PCL) as the outer layer of coaxial fibers, and polyethylene oxide (PEO) was as the core of the fibers. In this study, the effects of the concentration of MWCNTs and the coaxial structure on the performance of the NGCs were evaluated systematically. The results showed that the aligned MWCNTs loaded NGCs composed of coaxial fibers had excellent performance in nerve regeneration and functional recovery while providing suitable mechanical support for nerve regrowth when the loading concentration of MWCNTs was 1%. Therefore, the aligned MWCNTs loaded NGCs composed of coaxial fibers offers essential insights into the preparation of traditional NGCs and highlighted its potential in clinical application. © 2023, The Authors. All rights reserved.

关键词： Fibers

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Breast Image Mass Detection Model Based on Multi-Scale Feature Reinforcement Network

SSRN

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SSRN 2024年

作者： Wang, Xianzhe Li, Ying Guo, Hongxu He, Renjie Quan, Jie Wang, Lingyue Guo, Lei Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300131 China Tianjin Key Laboratory of Bioelectricity and Intelligent Health School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300131 China Department of Radiation Oncology MD Anderson Cancer Center Houston77030 United States Shenzhen Mindray Bio-Medical Electronics Co.Ltd Guangdong Shenzhen518052 China

Analyzing X-ray images is a critical method for diagnosing breast cancer. The prompt and accurate detection of masses from extensive image data is vital in reducing breast cancer incidence and mortality rates. While deep learning-based approaches show promise in effectively detecting cancer, they still face challenges. Specifically, these approaches struggle to extract features from small objects and lack multi-scale feature fusion. Addressing these issues is crucial for more robust detection. In this paper, we propose the Multi-scale Reinforcement Feature Pyramid Network (MRFPN) to tackle these challenges. Firstly, we introduce the Feedback Connections Enhance Module (FCEM), which integrates additional feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. We also design the Symmetry Complement Module (SCM) as a feedback connection structure. Additionally, we propose the Multiscale Fusion Strengthen Module (MFSM) to enhance the expressive power of each layer’s feature map by utilizing feature map information from multiple scales. Within the MFSM, we design the Receptive Expansion Module (REM), which employs dilated convolution to broaden the receptive field, thereby efficiently improving the network’s capability to detect objects of arbitrary sizes. Furthermore, by incorporating MRFPN into Faster RCNN, we design the Multi-scale Reinforcement Breast Mass Detection Network (MRBMDNet). Extensive experiments demonstrate that MRBMDNet outperforms the baseline on datasets of different qualities. Our method achieved 67.84% (in AP@50) and 96% (in TPR@20) on the commonly used INbreast dataset, and 87.5% and 63.2% (in TPR@20) on the publicly available BCS-DBT and MIAS datasets, respectively. Code is available at https://***/xianzhe-wang/MRFPN. © 2024, The Authors. All rights reserved.

关键词： Reinforcement

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