检索结果-内蒙古大学图书馆

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

来源：评论

学校读者我要写书评

暂无评论

Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground

引用

Scientific data 2018年第1期5卷 180133页

作者： Justin A Brantley Trieu Phat Luu Sho Nakagome Fangshi Zhu Jose L Contreras-Vidal Laboratory for Non-Invasive Brain Machine Interfaces Department of Electrical & Computer Engineering University of Houston Houston TX 77056 USA.

Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain's involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

Journal of Biomedical Science and engineering 2016年第8期9卷 393-398页

作者： David Steyrl Reinmar J. Kobler Gernot R. Müller-Putz Institute of Neural Engineering Laboratory of Brain-Computer Interfaces Graz University of Technology Graz Austria

We perceive that some brain-computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although... 详细信息

关键词： brain-computer interfaces Electrical brain Signals Invasive Signals Non-Invasive Signals Comparison

来源：评论

学校读者我要写书评

暂无评论

Comparing EEG, its time-derivative and their joint use as features in a BCI for 2-D pointer control

Comparing EEG, its time-derivative and their joint use as fe...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dimitrios Andreou Riccardo Poli Brain Computer Interfaces and Neural Engineering Laboratory University of Essex UK

Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse. Features were selected based on the absolute difference of medians of the target and non-target classes. Classification was carried out by an ensemble of linear support vector machines which were optimised using the mutual information criterion. Comparisons were based on the area under the receiver operating characteristics. The Mann-Whitney one-tailed test was used to study significance. Results show that EEG amplitudes are outperformed by both the first derivative and the combined feature vector and that derivatives are better than the combined vector.

关键词： Electroencephalography Feature extraction Electrodes Wavelet transforms Mice brain-computer interfaces Training

来源：评论

学校读者我要写书评

暂无评论

Correction to "Correlated Component Analysis for Enhancing the Performance of SSVEP-Based brain-computer Interface"

引用

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE engineering in Medicine and Biology Society 2018年第8期26卷 1645-1646页

作者： Yangsong Zhang Daqing Guo Fali Li Erwei Yin Yu Zhang Peiyang Li Qibin Zhao Toshihisa Tanaka Dezhong Yao Peng Xu Masaki Nakanishi School of Computer Science and Technology Southwest University of Science and Technology Mianyang China MOE Key Laboratory for Neuroinformation Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu China Academy of Military Sciences China National Institute of Defense Technology Innovation Beijing China Department of Psychiatry and Behaviour Sciences Stanford University Stanford CA USA Tensor Learning Unit RIKEN Center for Advanced Intelligence Project Tokyo Japan Department of Electronic and Information Engineering Tokyo University of Agriculture and Technology Tokyo Japan Swartz Center for Computational Neuroscience Institute for Neural Computation University of California at San Diego La Jolla CA USA

In the above paper [1], a method has been proposed to use the correlated component analysis (CORCA) to learn spatial filters with multiple blocks of individual training data for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) scenario. In order to evaluate the performance of CORCA, the task-related component analysis (TRCA)-based method was used as a baseline method [2]. For a fair and convincing comparison, the MATLAB codes on the website (https://***/mnakanishi/TRCA-SSVEP) for implementing TRCA method provided by Dr. Masaki Nakanishi, the first author of [2], were used to take the role of the TRCA method. At that time, the proposed CORCA-based method outperforms the TRCA-based method [1].

关键词： Bars Standards Analysis of variance brain-computer interfaces Task analysis Hospitals Electronic mail

来源：评论

学校读者我要写书评

暂无评论

Neuromatch academy: Teaching computational neuroscience with global accessibility

arXiv

引用

arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Optimal Feedback Control Framework Suggests that Changes in The Preferred Direction During BMI Experiments may Occur Even with no Adaptation

Optimal Feedback Control Framework Suggests that Changes in ...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Miri Benyamini Miriam Zacksenhouse Brain-computer interfaces for rehabilitation Laboratory Department of Mechanical Engineering Technion - IIT Haifa Israel

ISBN: (纸本)9781509018987

brain Machine interfaces (BMIs) rely on the correlation between neural activity and movement kinematics. However, many characteristics of the neural activity change after switching from pole control to brain control. Of particular interest are changes in the preferred direction (PD), and whether they reflect adaptation to the BMI filter. Here we investigate changes in the PD of simulated neurons that encode signals that are relevant for state estimation and control with the framework of optimal feedback control (OFC). Simulated BMI experiments based on the OFC framework demonstrate that changes in the PD may occur even with no adaptation. Further theoretical and simulations indicate the conditions under which there is no change in the PD upon switching to brain control. . Insights gained from this research can be used to improve the design of BMI filter - not only to minimize reconstruction error during pole control, but also to endow the neurons with desired PDs in brain control.

关键词： Computational motor control brain-machine interfaces neural modulations optimal feedback control

来源：评论

学校读者我要写书评

暂无评论

National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

引用

NATURE COMMUNICATIONS 2022年第1期13卷 1-14页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA Department of Psychology University of Amsterdam Amsterdam Netherlands School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Center for Advanced Hindsight Duke University Durham NC USA Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed B

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1,N= 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N= 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r= −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：