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Diffusion dynamics of a conductance-based neuronal population

Physical Review E 2019年第4期99卷 042307-042307页

作者： Argha Mondal Sanjeev Kumar Sharma Ranjit Kumar Upadhyay M. A. Aziz-Alaoui Prosenjit Kundu Chittaranjan Hens Department of Applied Mathematics Indian Institute of Technology (Indian School of Mines) Dhanbad 826004 India Computational Neuroscience Center University of Washington Seattle Washington 98195 USA UniHavre LMAH CNRS No. 3335 ISCN Normandie University 76600 Le Havre France National Institute of Technology Durgapur Durgapur West Bengal 713209 India Physics and Applied Mathematics Unit Indian Statistical Institute Barrackpore Trunk Road Kolkata West Bengal 700108 India

We study the spatiotemporal dynamics of a conductance-based neuronal cable. The processes of one-dimensional (1D) and 2D diffusion are considered for a single variable, which is the membrane voltage. A 2D Morris-Lecar (ML) model is introduced to investigate the nonlinear responses of an excitable conductance-based neuronal cable. We explore the parameter space of the uncoupled ML model and, based on the bifurcation diagram (as a function of stimulus current), we analyze the 1D diffusion dynamics in three regimes: phasic spiking, coexistence states (tonic spiking and phasic spiking exist together), and a quiescent state. We show (depending on parameters) that the diffusive system may generate regular and irregular bursting or spiking behavior. Further, we explore a 2D diffusion acting on the membrane voltage, where striped and hexagonlike patterns can be observed. To validate our numerical results and check the stability of the existing patterns generated by 2D diffusion, we use amplitude equations based on multiple-scale analysis. We incorporate 1D diffusion in an extended 3D version of the ML model, in which irregular bursting emerges for a certain diffusion strength. The generated patterns may have potential applications in nonlinear neuronal responses and signal transmission.

关键词： Bifurcations Emergence of patterns Neuronal networks Pattern formation

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Ten quick tips for deep learning in biology

arXiv

引用

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Reply to “Comment on ‘Impurity spectra of graphene under electric and magnetic fields' ”

引用

Physical Review B 2018年第20期97卷 207404-207404页

作者： Jia-Lin Zhu Guo Li Ning Yang State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

In this reply to R. Van Pottelberge, M. Zarenia, and F. M. Peeters's comment on our original paper, “Impurity spectra of graphene under electric and magnetic fields,” we discuss the existence of bound states of a Coulomb impurity in graphene through the regular and irregular solution of the Dirac equation, and clarify its relation to the term “atom collapse” in our original paper.

关键词： Graphene

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Heterogeneous inputs to central pattern generators can shape insect gaits.

arXiv

引用

arXiv 2018年

作者： Aminzare, Zahra Holmes, Philip Program in Applied and Computational Mathematics Princeton University NJ United States Program in Applied and Computational Mathematics Department of Mechanical and Aerospace Engineering Princeton Neuroscience Institute Princeton University NJ United States

In our previous work [1], we studied an interconnected bursting neuron model for insect locomotion, and its corresponding phase oscillator model, which at high speed can generate stable tripod gaits with three legs off the ground simultaneously in swing, and at low speed can generate stable tetrapod gaits with two legs off the ground simultaneously in swing. However, at low speed several other stable locomotion patterns, that are not typically observed as insect gaits, may coexist. In the present paper, by adding heterogeneous external input to each oscillator, we modify the bursting neuron model so that its corresponding phase oscillator model produces only one stable gait at each speed, specifically: a unique stable tetrapod gait at low speed, a unique stable tripod gait at high speed, and a unique branch of stable transition gaits connecting them. This suggests that control signals originating in the brain and central nervous system can modify gait patterns.34C15, 34C60, 37G10, 92B20, 92C20 Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Ordered magnetic fields around the 3C 84 central black hole

arXiv

引用

arXiv 2024年

作者： Paraschos, Georgios Filippos Kim, J.-Y. Wielgus, M. Röder, J. Krichbaum, T.P. Ros, E. Agudo, I. Myserlis, I. Moscibrodzka, M. Traianou, E. Zensus, J.A. Blackburn, L. Chan, C.-K. Issaoun, S. Janssen, M. Johnson, M.D. Fish, V.L. Akiyama, K. Alberdi, A. Alef, W. Algaba, J.C. Anantua, R. Asada, K. Azulay, R. Bach, U. Baczko, A.-K. Ball, D. Baloković, M. Barrett, J. Bauböck, M. Benson, B.A. Bintley, D. Blundell, R. Bouman, K.L. Bower, G.C. Boyce, H. Bremer, M. Brinkerink, C.D. Brissenden, R. Britzen, S. Broderick, A.E. Broguiere, D. Bronzwaer, T. Bustamante, S. Byun, D.-Y. Carlstrom, J.E. Ceccobello, C. Chael, A. Chang, D.O. Chatterjee, K. Chatterjee, S. Chen, M.T. Chen, Y. Cheng, X. Cho, I. Christian, P. Conroy, N.S. Conway, J.E. Cordes, J.M. Crawford, T.M. Crew, G.B. Cruz-Osorio, A. Cui, Y. Dahale, R. Davelaar, J. De Laurentis, M. Deane, R. Dempsey, J. Desvignes, G. Dexter, J. Dhruv, V. Doeleman, S.S. Dougal, S. Dzib, S.A. Eatough, R.P. Emami, R. Falcke, H. Farah, J. Fomalont, E. Ford, H.A. Foschi, M. Fraga-Encinas, R. Freeman, W.T. Friberg, P. Fromm, C.M. Fuentes, A. Galison, P. Gammie, C.F. García, R. Gentaz, O. Georgiev, B. Goddi, C. Gold, R. Gómez-Ruiz, A.I. Gómez, J.L. Gu, M. Gurwell, M. Hada, K. Haggard, D. Haworth, K. Hecht, M.H. Hesper, R. Heumann, D. Ho, L.C. Ho, P. Honma, M. Huang, C.L. Huang, L. Hughes, D.H. Ikeda, S. Impellizzeri, C.M.V. Inoue, M. James, D.J. Jannuzi, B.T. Jeter, B. Jaing, W. Jiménez-Rosales, A. Jorstad, S. Joshi, A.V. Jung, T. Karami, M. Karuppusamy, R. Kawashima, T. Keating, G.K. Kettenis, M. Kim, D.-J. Kim, J. Kim, J. Kino, M. Koay, J.Y. Kocherlakota, P. Kofuji, Y. Koch, P.M. Koyama, S. Kramer, C. Kramer, J.A. Kramer, M. Kuo, C.-Y. La Bella, N. Lauer, T.R. Lee, D. Lee, S.-S. Leung, P.K. Levis, A. Li, Z. Lico, R. Lindahl, G. Lindqvist, M. Lisakov, M. Liu, J. Liu, K. Liuzzo, E. Lo, W.-P. Lobanov, A.P. Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy and Atmospheric Sciences Kyungpook National University Daegu702-701 Korea Republic of Institute of Physics Silesian University in Opava Bezručovo nám. 13 OpavaCZ-746 01 Czech Republic Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Avenida Divina Pastora 7 Local 20 GranadaE-18012 Spain Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States NASA Hubble Fellowship Program Einstein Fellow United States Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 València PaternaE-46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-43992 Sweden Yale C

Context. 3C 84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of very-long-baseline interferometry (VLBI) above the hitherto available maximum frequency of 86 GHz. Aims. Using ultrahigh resolution VLBI observations at the currently highest available frequency of 228 GHz, we aim to perform a direct detection of compact structures and understand the physical conditions in the compact region of 3C 84. Methods. We used Event Horizon Telescope (EHT) 228 GHz observations and, given the limited (u, v)-coverage, applied geometric model fitting to the data. Furthermore, we employed quasi-simultaneously observed, ancillary multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. Results. We report the detection of a highly ordered, strong magnetic field around the central, supermassive black hole of 3C 84. The brightness temperature analysis suggests that the system is in equipartition. We also determined a turnover frequency of νm = (113 ± 4) GHz, a corresponding synchrotron self-absorbed magnetic field of BSSA = (2.9 ± 1.6) G, and an equipartition magnetic field of Beq = (5.2 ± 0.6) G. Three components are resolved with the highest fractional polarisation detected for this object (mnet = (17.0 ± 3.9)%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228 GHz. We used these findings to test existing models of jet formation, propagation, and Faraday rotation in 3C 84. Conclusions. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C 84. However,

关键词： Magnetic fields

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2022 Review of Data-Driven Plasma Science

arXiv

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Criterion and principles of easily-modified program

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IOP Conference Series: Materials Science and Engineering 2021年第1期1027卷

作者： A Prutzkow Department of Computational and Applied Mathematics Ryazan State Radio Engineering University named after V. F. Utkin 59/1 Gagarina str. Ryazan 390005 Russian Federation Department of Mathematics Physics and Medical Computer Sciences Ryazan State Medical University named after I. P. Pavlov 9 Vysokovoltnaya str. Ryazan 390026 Russian Federation

Ease of modifying is one of the characteristics of the program. The existing approaches do not clarify this concept. These approaches are clean code and maintainability. We formulate simple and explicit criterion and principles of easily-modified programs. The criterion is an E.W. Dijkstra's criterion. The principles are (1) the names of the elements should reflect their purpose in the program; (2) use indentation for nested statements and blank lines; (3) one program element must have one assignment and one assignment must correspond to only one element; (4) optimize dependencies between program elements. We introduce a metric for easily-modified programs as well. It's a number of commands in methods and static blocks in a class.

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Interface-driven unusual anomalous Hall effect in MnxGa/Pt bilayers

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Physical Review B 2019年第18期100卷 184410-184410页

作者： K. K. Meng L. J. Zhu Z. H. Jin E. K. Liu X. P. Zhao I. A. Malik Z. G. Fu Y. Wu J. Miao X. G. Xu J. X. Zhang J. H. Zhao Y. Jiang Beijing Advanced Innovation Center for Materials Genome Engineering School of Materials Science and Engineering University of Science and Technology Beijing Beijing China Cornell University Ithaca New York 14853 USA Department of Applied Physics Graduate School of Engineering Tohoku University 6-6-05 Aoba-yama 980-8579 Sendai Japan Institute of Physics Chinese Academy of Sciences Beijing China State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing China Department of Physics Beijing Normal University Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

The effects of spin-orbit coupling and symmetry breaking at the interface between a ferromagnet and heavy metal are particularly important for spin-based information storage and computation. Recent discoveries suggest they can create novel chiral spin structures (e.g., skyrmions), which have often been identified through the appearance of the bump/dip features of Hall signals, the so-called topological Hall effect (THE). In this work, however, we present an unusual anomalous Hall effect (UAHE) in MnxGa/Pt bilayers and demonstrate that the features extremely similar to THE can be generated without involving any chiral spin structures. Low-temperature magnetic force microscopy has been used to explore the magnetic field-dependent behavior of spin structures, and the UAHE as a function of magnetic field does not peak near the maximal density of magnetic bubbles. Our results unambiguously evidence that the UAHE in MnxGa/Pt bilayers shows no correlation with chiral spin structures but is driven by the modified interfacial properties, indicating a wealth of underlying and interesting physics.

关键词： Anomalous Hall effect Spin-orbit coupling Topological Hall effect

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Well-posedness for energy-critical nonlinear schrödinger equation on waveguide manifold

arXiv

引用

arXiv 2019年

作者： CHENG, XING ZHAO, ZEHUA ZHENG, JIQIANG College of Science Hohai University Nanjing Jiangsu210098 China Department of Mathematics University of Maryland William E. Kirwan Hall 4176 Campus Dr. College ParkMD20742-4015 United States Institute of Applied Physics and Computational Mathematics Beijing100088 China

In this article, we utilize the scale-invariant Strichartz estimate on waveguide which is developed recently by Barron [1] based on Bourgain-Demeter l2 decoupling method [4] to give a unified and simpler treatment of well-posedness results for energy critical nonlinear Schrödinger equation on waveguide when the whole dimension is three and *** Codes 35Q55 (Primary), 35R01, 58J50, 47A40 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Waveguides

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Three-scale singular limits of evolutionary PDEs

arXiv

引用

arXiv 2019年

作者： Cheng, Bin Ju, Qiangchang Schochet, Steve Department of Mathematics University of Surrey GuildfordGU2 7XH United Kingdom Institute of Applied Physics and Computational Mathematics P.O. Box 8009 Beijing100088 China School of Mathematical Sciences Tel-Aviv University Tel Aviv69978 Israel

Singular limits of a class of evolutionary systems of partial differential equations having two small parameters and hence three time scales are considered. Under appropriate conditions solutions are shown to exist and remain uniformly bounded for a fixed time as the two parameters tend to zero at different rates. A simple example shows the necessity of those conditions in order for uniform bounds to hold. Under further conditions the solutions of the original system tend to solutions of a limit equation as the parameters tend to *** Codes 35B25, 35L45 Copyright © 2019, The Authors. All rights reserved.

关键词： Partial differential equations

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