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A high resolution PVDF (peizoelectric) film respiration sensor

AIP Conference Proceedings 2017年第1期1865卷

作者： Katsuya Nakano Kento Fujita Shinya Misaki Hiroyuki Fujii Robert Johnston Yukinori Misaki 1Advanced Course in Electronics Information and Communication Engineering National Institute of Technology Kagawa College Takuma Campus Mitoyo City Kagawa Prefecture JAPAN 2Department of Electronic Systems Engineering National Institute of Technology Kagawa College Takuma Campus Mitoyo City Kagawa Prefecture JAPAN

Sensors used today for contact measurement of a subject’s breathing work by measuring the inductance change in some film, piezoelectric or pyro-electric, used in the sensor. However, their use can increase stress and burden for patients because of the close proximity to the body that the sensors must be to operate. They must be applied directly to the patient’s body by tape or adhesive paste. To address this problem and reduce subject stress and burden, it was decided to research development of a high resolution breathing sensor that could still function even while placed over the patient’s clothes. This was achieved by developing a new PVDF piezoelectric film based sensor with an innovative configuration. Through the use of some simple amplification circuitry and processing the output signal, the high sensitivity breathing sensor developed was determined to be able to accurately measure a person’s breathing. Also, due to the high sensitivity of the sensor, heart rate was also detectable revealing the possibility for simultaneous measurement of both breathing and heart rate.

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Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

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Astrophysical Journal, Supplement Series 2023年第2期267卷 29-29页

作者： Abbott, R. Abe, H. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Almualla, M. Altin, P.A. Amato, A. Amez-Droz, L. Amorosi, A. Anand, S. Ananyeva, A. Andersen, R. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Apostolatos, T. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Arogeti, M. Aronson, S.M. Arun, K.G. Asada, H. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Aubin, F. AultONeal, K. Babak, S. Badalyan, A. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bai, Y. Baier, J.G. Baiotti, L. Baird, J. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Baltus, G. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barber, J. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Barthelmy, S.D. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Beirnaert, F. Bejger, M. Bell, A.S. Benedetto, V. Beniwal, D. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhandari, A.V. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bianchi, A. Bilenko, I.A. Bilicki, M. Billingsley, G. Bini, S. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boër, M. Bogaert, G. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, G.S. Bonnand, R. Booker, P. Bork, R. Boschi, V. Bose, N. LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan Dipartimento di Farmacia Università di Salerno Salerno Fisciano I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy INFN Sezione di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA Australia Univ. Savoie Mont Blanc CNRS Laboratoire d'Annecy de Physique des Particules - IN2P3 Annecy F-74000 France Università di Napoli “Federico II” Napoli I-80126 Italy University of Minnesota Minneapolis 55455 MN United States M

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://***. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. © 2023. The Author(s). Published by the American Astronomical Society.

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Hands-free pointing system using a microlens array marker

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2015年第1期53卷 44-49页

作者： Tsukada, Akira Tanaka, Hideyuki Himeno, Rieko Department of Electronic and Computer Engineering National Institute of Technology Toyama College Japan Intelligent Systems Research Institute National Institute of Industrial Science and Technology Japan Department of Advanced Course National Institute of Technology Toyama College Japan

People with upper limb disabilities often need to use auxiliary equipment or alternative input methods to operate personal computers. Instead of using a traditional mouse, they may employ methods that use a numeric keypad supported by a function of MouseKeys, manipulate a joystick or trackball using their residual abilities, or use a mouth stick or head stick to control the mouse. Each of these methods requires a lot of time and effort. In this report, the authors propose a new pointing method that uses a high-precision visual marker (ArrayMark) developed in the robotic field. The marker uses a microlens array and overcomes the primary problem confronted by conventional markers: reduced pose-estimation precision in frontal observations. A user mounts the marker on the head and turns the face toward the display. The proposed system estimates head position and pose, and synchronizes a cursor with facial direction. In precision experiments, when an able-bodied subject fixed his face toward a target for 3.3 seconds at a distance of 700 mm, the standard deviation error for the cursor coordinates was within 1.7 mm. The times required for pointing when using the proposed method, mouse, and MouseKeys were compared. The proposed method took 1.8 times more time than the mouse and 0.36 times less time than the MouseKeys. © 2015, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Auxiliary equipment

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Design and kinematic simulation of a novel exoskeleton rehabilitation hand robot

Design and kinematic simulation of a novel exoskeleton rehab...

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IEEE International Conference on Mechatronics and Automation

作者： Shuxiang Guo Weijie Zhang Jian Guo Jiange Gao Yuye Hu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Biomedical Robot Laboratory Tianjin University of Technology Tianjin China Intelligent Mechanical Systems Engineering Department Kagawa University Takamatsu Japan Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin China

ISBN: (纸本)9781509023974

The rehabilitation exoskeleton robot is more and more used in the assisting stroke patients in implementing rehabilitation training. In this paper, a novel exoskeleton hand robot which was driven by cable has been proposed to aim at helping varieties of paralyses patients recover motor function. This exoskeleton hand rehabilitation robot system mainly consists of exoskeleton hand robot, EEG system, motor controllers unit, some sensors and a workstation. And the hand exoskeleton mechanism was portable, wearable and adjustable for patients doing home rehabilitation training. Based on the parameters from human hand, the kinematic model of hand robot was established to be used in designing and the kinematic simulation experiments about this exoskeleton hand robot were done. Through the simulation software ADAMS (Automatic Dynamic Analysis of Mechanical systems), the parameters of position, angle velocity and angle accelerated velocity (PVA) of proximate inter-phalanger (DIP) distal inter-phalanger joint were simulated. And then, the movement situation of the DIP joint was obtained according to the data from the position encoder. So the velocity curve and accelerated velocity curve were obtained by calculating the actual position function derivation. The simulated results showed that this exoskeleton hand robot has high movement ability to finish the continuous passive motion (CPM). Beside that, a comparison test was done to verify whether there were some motion blocks in wearing exoskeleton robot. Through analyzing the experimental data, the error between the actual position and the simulated position of the PIP joint of the finger was very small which verified the small interference of robot. These experiments demonstrated that the exoskeleton hand robot has high efficiency movement ability for stroke patients who need to do the finger rehabilitation training. In the future, the optimization design of the exoskeleton hand robot will be done in order to improve more mov

关键词： Exoskeletons Thumb Robot sensing systems Joints Training

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Design of the structural optimization for the upper limb rehabilitation robot

Design of the structural optimization for the upper limb reh...

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IEEE International Conference on Mechatronics and Automation

作者： Shuxiang Guo Jiange Gao Jian Guo Weijie Zhang Yuye Hu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Biomedical Robot Laboratory Tianjin University of Technology Tianjin China Intelligent Mechanical Systems Engineering Department Kagawa University Takamatsu Japan Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin China

ISBN: (纸本)9781509023974

The exoskeleton robotic rehabilitation training system is a branch of the field of robotics, it is the external mechanical system that is consistent with the similar joints of the human body, and attached to the human body. With the development of exoskeleton rehabilitation robotic system, the biomechanics influence of device should be considered in rehabilitation training. This paper proposes a novel upper limb exoskeleton rehabilitation device based on biomechanics, which can be used in rehabilitation of upper limb for hemi paralysis patients. This system aims at helping hemi paralysis patients recover motor function of upper limb; in addition it is suitable for variety of patients. This system is portable and wearable, which consists of exoskeleton device, hepatic device (PHANTOM Premium), motor controllers and work station (computer). This paper proposes a structural optimization scheme in order to solve some problems of bowl's structure of the rehabilitation robot. The Pro/ENGINEER Wildfire (Pro/E) software is applied to get a three-dimensional modelling, kinematics simulation and dynamic analysis of the rehabilitation robot. From the analysis, the robot end motion simulation curves were obtained to verify the optimization structure reasonable and better motion characteristics. In the future, this structure will have a wide application prospect in the rehabilitation therapy field.

关键词： Robots Exoskeletons Wrist Jacobian matrices Gears Optimization Acceleration

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Biaxial tensile deformation simulation of 5000 series aluminum alloy sheet using crystal plasticity finite element method based on homogenization method and its experimental validation

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Keikinzoku/Journal of Japan Institute of Light Metals 2015年第5期65卷 196-203页

作者： Hashimoto, Keisuke Yamanaka, Akinori Kawaguchi, Junpei Sakurai, Takeo Kuwabara, Toshihiko Department of Mechanical Systems Engineering Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei-shiTokyo184-8588 Japan Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology Koganei-shiTokyo Japan Technology Control Department Aluminum and Copper Business Kobe Steel Ltd. Shinagawa-kuTokyo Japan

Biaxial tensile deformation behavior of 5000 series aluminum alloy sheet was simulated by using crystal plasticity finite element method based on the mathematical homogenization method. The representative volume element of crystallographic texture in the alloy was constructed using the crystal orientation data measured by electron back scattered diffraction technique. The material parameters used for the work hardening law were identified on the basis of the true stress-true strain curve measured by the uniaxial tensile test. In order to validate the simulated biaxial tensile deformation behavior of the alloy sheet under linear biaxial stress paths, the calculated true stress-true plastic strain curves and contours of equal plastic work in stress space were precisely compared with those obtained by a servo-controlled biaxial tensile testing machine. Although the anisotropic hardening behavior observed by the experiments was not fully reproduced by the present simulation, the simulated stress-strain curve and the evolution of contours of equal plastic work showed good agreement with the experimental result. © 2015 The Japan Institute of Light Metals.

关键词： Tensile testing

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Contributions of Social Manufacturing to Sustainable Apparel Industry

Contributions of Social Manufacturing to Sustainable Apparel...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Babak Mohajeri Timo Nyberg Jesse Karjalainen Mark Nelson Gang Xiong Aalto University Department of Industrial Engineering and Management Human Sciences and Technologies Advanced Research Institute (H-STAR)Stanford University The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Science

The global apparel and textile market has been changed considerably during the recent years. At the one hand, new trends and consumer preferences for different styles and fashion products emerge continuously, thus the apparel industry becomes more mass customized. On the other hand, attention to social and environmental sustainability has been dramatically increased in the entire industry. However, the current fast-changing fashion trends result to significant environmental and resource depletion problems in the textile lifecycle. As a result, the apparel industry should consider sustainable manufacturing strategies. The current options such as organic fibers and environmentally safe dyes seem promising. Nonetheless, there are significant challenges for apparel manufacturers and retailers to capitalize sustainability. To address these problems, in this paper, we develop a conceptual model of Social Manufacturing for the apparel industry. We study the current value chain of the apparel industry and identify its challenges from the sustainability's view. Then, we propose a new model to improve the sustainability throughout the value chain of the apparel industry

关键词： Social Manufacturing Apparel Industry Value chain sustainability

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Development of draw-bending testing method using digital image correlation system

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AIP Conference Proceedings 2017年第1期1896卷

作者： Chiharu Sekiguchi Tomoyuki Hakoyama Toshihiko Kuwabara Hiroshi Fukiharu 1JSPS Research Fellow (Doctoral course student) Graduate School Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo Japan 2RIKEN Center for Advanced Photonics RIKEN 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan 3Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo Japan 4Engineering Technology Division CAE Technology Department JSOL Corporation 2-5-24 Harumi Chuo-ku Tokyo Japan

A novel draw-bending testing method using a non-contact optical 3D deformation measuring system (ARAMIS®, GOM) was developed to investigate the deformation process of draw-bending. Furthermore, the accuracy of the fracture prediction based on a fracture criterion using the concept of forming limit stress was also investigated. The experimental apparatus is capable of applying draw-bending to a sheet specimen with a forming speed of 100 mm/s, which is comparable to those in real press forming operations. Specimens undergo bending-unbending under tension when passing over the die profile radius. The test materials is a 590MPa high-strength steel sheet. In order to evaluate the effect of the stress variation in the width direction of a specimen on the accuracy of the calculated draw-bending fracture stress σDB, the strain components on the upper surface of the specimen were measured using the ARAMIS® software. High-speed CMOS cameras were used to take synchronized images. The development of the strain field on the surface of the specimen was succssesfully measured. The σDB was calculated from the draw-bending force and the strain components at the moment immediately before fracture, and compared with the forming limit stress σPT measured for monotonic plane strain tension. It is concluded that the forming limit stress criterion is effective as a fracture criterion for the draw-bending of the test material.

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

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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

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