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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

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Psychiatry Research 2024年 342卷 115972-115972页

作者： Solmi, Marco Thompson, Trevor Estradé, Andrés Agorastos, Agorastos Radua, Joaquim Cortese, Samuele Dragioti, Elena Vancampfort, Davy Thygesen, Lau Caspar Aschauer, Harald Schlögelhofer, Monika Aschauer, Elena Schneeberger, Andres Huber, Christian G. Hasler, Gregor Conus, Philippe Cuénod, Kim Q. Do von Känel, Roland Arrondo, Gonzalo Fusar-Poli, Paolo Gorwood, Philip Llorca, Pierre-Michel Krebs, Marie-Odile Scanferla, Elisabetta Kishimoto, Taishiro Rabbani, Golam Skonieczna-Żydecka, Karolina Brambilla, Paolo Favaro, Angela Takamiya, Akihiro Zoccante, Leonardo Colizzi, Marco Bourgin, Julie Kamiński, Karol Moghadasin, Maryam Seedat, Soraya Matthews, Evan Wells, John Vassilopoulou, Emilia Gadelha, Ary Su, Kuan-Pin Kwon, Jun Soo Kim, Minah Lee, Tae Young Papsuev, Oleg Manková, Denisa Boscutti, Andrea Gerunda, Cristiano Saccon, Diego Righi, Elena Monaco, Francesco Croatto, Giovanni Cereda, Guido Demurtas, Jacopo Brondino, Natascia Veronese, Nicola Enrico, Paolo Politi, Pierluigi Ciappolino, Valentina Pfennig, Andrea Bechdolf, Andreas Meyer-Lindenberg, Andreas Kahl, Kai G. Domschke, Katharina Bauer, Michael Koutsouleris, Nikolaos Winter, Sibylle Borgwardt, Stefan Bitter, Istvan Balazs, Judit Czobor, Pál Unoka, Zsolt Mavridis, Dimitris Tsamakis, Konstantinos Bozikas, Vasilios P. Tunvirachaisakul, Chavit Maes, Michael Rungnirundorn, Teerayuth Supasitthumrong, Thitiporn Haque, Ariful Brunoni, Andre R. Costardi, Carlos Gustavo Schuch, Felipe Barreto Polanczyk, Guilherme Luiz, Jhoanne Merlyn Fonseca, Lais Aparicio, Luana V. Valvassori, Samira S. Nordentoft, Merete Vendsborg, Per Hoffmann, Sofie Have Sehli, Jihed Sartorius, Norman Heuss, Sabina Guinart, Daniel Hamilton, Jane Kane, John Rubio, Jose Sand, Michael Koyanagi, Ai Solanes, Aleix Andreu-Bernabeu, Alvaro Cáceres, Antonia San José Arango, Celso Díaz-Caneja, Covadonga M. Hidalgo-Mazzei, Diego Vieta, Eduard Gonzalez-Peñas, Javier Fortea, Lydia Parellada, Mara Fullana, Miquel A. Verdolini, Norma Andrlíková, Eva Janků, Karolina Millan, Mark J. Honciuc, Mihaela Moniuszko-Mal University of Ottawa Department of Psychiatry ON Canada The Ottawa Hospital Department of Mental Health ON Canada Ottawa Hospital Research Institute (OHRI) Neurosciences Program Ottawa Ontario Canada Early Psychosis: Interventions and Clinical-detection (EPIC) Lab Department of Psychosis Studies Institute of Psychiatry Psychology & Neuroscience King's College London London United Kingdom Centre for Innovation in Mental Health-Developmental Lab School of Psychology University of Southampton and NHS Trust Southampton United Kingdom Charité Universitätsmedizin Berlin Department of Child and Adolescent Psychiatry Berlin Germany Centre for Chronic Illness and Ageing University of Greenwich London United Kingdom University of Southampton Centre for Innovation in Mental Health Southampton United Kingdom Universidad Católica Department of Psychology Montevideo Uruguay Aristotle University of Thessaloniki II. Dept. of Psychiatry Division of Neurosciences Medical School Faculty of Health Sciences Greece Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Imaging of Mood- and Anxiety-Related Disorders (IMARD) University of Barcelona CIBERSAM Instituto de Salud Carlos III Barcelona Spain Karolinska Institutet Centre for Psychiatric Research and Education Department of Clinical Neuroscience Stockholm Sweden Linköping University Pain and Rehabilitation Centre and Department of Health Medicine and Caring Sciences Linköping Sweden University of Ioannina Research Laboratory Psychology of Patients Families & Health Professionals Department of Nursing School of Health Sciences Ioannina Greece Katholieke Universiteit Leuven (KU Leuven) Department of Rehabilitation Sciences Leuven Belgium National Institute of Public Health University of Southern Denmark Denmark BioPsyC - Biopsychosocial Corporation Non-profit association for Research Funding Ltd. Vienna Austria University of California San Diego California United States University of Basel Universitä

International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ², penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies. © 2024 The Authors

关键词： Covid-19 Mental health P-factor Pandemic Psychiatry Psychopathology Survey Well-being WHO-5

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

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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

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Differential covariance: A new method to estimate functional connectivity in fMRI

arXiv

引用

arXiv 2017年

作者： Lin, Tiger W. Krishnan, Giri P. Bazhenov, Maxim Sejnowski, Terrence J. Howard Hughes Medical Institute Computational Neurobiology Laboratory Salk Institute for Biological Studies San DiegoCA92037 United States Neurosciences Graduate Program University of California San Diego San DiegoCA92092 United States Institute for Neural Computation University of California San Diego San DiegoCA92092 Department of Medicine University of California San Diego San DiegoCA92092 United States

Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used methods are prone to produce false connections. We introduce here a new method that uses derivative for estimating functional connectivity. Using simulations, we benchmarked our method with other commonly used methods. Our method achieves better results in complex network simulations. This new method provides an alternative way to estimate functional connectivity. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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Differential covariance: A new class of methods to estimate sparse connectivity from neural recordings

arXiv

引用

arXiv 2017年

作者： Lin, Tiger W. Das, Anup Krishnan, Giri P. Bazhenov, Maxim Sejnowski, Terrence J. Howard Hughes Medical Institute Computational Neurobiology Laboratory Salk Institute for Biological Studies San DiegoCA92037 United States Neurosciences Graduate Program University of California San Diego San DiegoCA92092 United States Institute for Neural Computation University of California San Diego San DiegoCA92092 Department of Medicine University of California San Diego San DiegoCA92092 United States Jacobs School of Engineering University of California San Diego San DiegoCA92092 United States

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson et al., 2008), they produce spurious connections. The general linear model (GLM), which models spikes trains as Poisson processes (Okatan et al., 2005;Truccolo et al., 2005;Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential (LFP) recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. Copyright © 2017, The Authors. All rights reserved.

关键词： Electrophysiology

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Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro Xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh Xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora X Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

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Publisher Correction: Tuned thalamic excitation is amplified by visual cortical circuits

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Nature neuroscience 2018年第9期21卷 1292页

作者： Anthony D Lien Massimo Scanziani Neurosciences Graduate Program University of California San Diego La Jolla California USA. anthony.d.lien@***. Center for Neural Circuits and Behavior Neurobiology Section and Department of Neuroscience University of California San Diego La Jolla California USA. anthony.d.lien@***. Howard Hughes Medical Institute University of California San Diego La Jolla California USA. anthony.d.lien@***. Neurosciences Graduate Program University of California San Diego La Jolla California USA. massimo@ucsd.edu. Center for Neural Circuits and Behavior Neurobiology Section and Department of Neuroscience University of California San Diego La Jolla California USA. massimo@ucsd.edu. Howard Hughes Medical Institute University of California San Diego La Jolla California USA. massimo@ucsd.edu.

In the published version of this article, a data point is missing from Fig. 4f, and the y-axis label reads "RF"; it should read "RF." The original article has not been corrected. The original and c...

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Self-sustained activity in balanced networks with low firing-rate

arXiv

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arXiv 2018年

作者： Borges, F.S. Protachevicz, P.R. Pena, R.F.O. Lameu, E.L. Matias, F.S. Higa, G.S.V. Kihara, A.H. Antonopoulos, C.G. Iarosz, K.C. de Pasquale, R. Roque, A.C. Batista, A.M. Ji, P. Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do Campo SP Brazil Institute of Physics University of São Paulo São Paulo SP Brazil Graduate in Science Program - Physics State University of Ponta Grossa PR Brazil Laboratory of Neural Systems Department of Physics University of São Paulo Ribeirão Preto SP Brazil National Institute for Space Research São José dos Campos SP Brazil Department of Physics Humboldt University Berlin Germany Cognitive Neuroimaging Unit CEA DRF/I2BM INSERM Université Paris-Sud Université Paris-Saclay Gif/YvetteF-91191 France Institute of Physics Federal University of Alagoas Maceió AL Brazil Department of Mathematical Sciences University of Essex Wivenhoe Park United Kingdom Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physiology and Biophysics ICB University of São Paulo São Paulo SP Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa PR Brazil Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China

The brain can display self-sustained activity (SSA), which is persistent firing of neurons in the absence of external stimuli. This spontaneous activity shows low neuronal firing rates and is observed in diverse in vitro and in vivo situations. In this work, we study the influence of excitatory/inhibitory balance, connection density, and network size on the self-sustained activity of a neuronal network model. We build a random network of adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses. The AdEx model mimics several behaviours of biological neurons, such as spike initiation, adaptation, and bursting patterns. In an excitation/inhibition balanced state, if the mean connection degree (K) is fixed, the firing rate does not depend on the network size (N), whereas for fixed N, the firing rate decreases when K increases. However, for large K, SSA states can appear only for large N. We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and spike-train power spectra with slow fluctuations, only for balanced networks of large size. Copyright © 2018, The Authors. All rights reserved.

关键词： neural networks

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

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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

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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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