Computational Neuroscience: Mathematical and Statistical Perspectives

Robert E. Kass; Shun-Ichi Amari; Kensuke Arai; Emery N. Brown; Casey O. Diekman; Markus Diesmann; Brent Doiron; Uri T. Eden; Adrienne L. Fairhall; Grant M. Fiddyment

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Computational Neuroscience: Mathematical and Statistical Perspectives

机译：计算神经科学：数学和统计观点

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Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

机译：数学和统计模型在神经科学中发挥了重要作用，尤其是通过描述单独记录的神经元的电活动，或者在大型网络上集体。随着领域迅速向前发展，新挑战正在出现。对于最大效率，努力推进计算神经科学的人需要欣赏和利用机械理论的互补优势和统计范式。

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来源
《Annual Review of Statistics and Its Application》 |2018年第2018期|共32页
作者
Robert E. Kass; Shun-Ichi Amari; Kensuke Arai; Emery N. Brown; Casey O. Diekman; Markus Diesmann; Brent Doiron; Uri T. Eden; Adrienne L. Fairhall; Grant M. Fiddyment;
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作者单位

Department of Statistics Machine Learning Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA;

Mathematical Neuroscience Laboratory RIKEN Brain Science Institute Wako Saitama Prefecture 351-0198 Japan;

Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA;

Department of Anesthesia Harvard Medical School Boston Massachusetts 02115 USA;

Department of Mathematical Sciences New Jersey Institute of Technology Newark New Jersey 07102 USA;

Institute of Neuroscience and Medicine Julich Research Centre 52428 Julich Germany;

Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA;

Department ofMathematics UniversityofPittsburgh Pittsburgh Pennsylvania 15260 USA;

Department of Physiology and Biophysics University of Washington Seattle Washington98105 USA;

Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
Lneural data analysis; neural modeling; neural networks; theoretical neuroscience;

机译：血管数据分析;神经建模;神经网络;理论神经科学;

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1. Computational Neuroscience: Mathematical and Statistical Perspectives [J] . Robert E. Kass, Shun-Ichi Amari, Kensuke Arai, Annual Review of Statistics and Its Application . 2018,第期

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2. NeuroBox: computational mathematics in multiscale neuroscience [J] . M. Stepniewski, M. Breit, M. Hoffer, Computing and visualization in science . 2019,第3a6期

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3. Book Review: "Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems", P. Dayan and L. F. Abbott, eds., (2001) [J] . Roman R. Poznanski Journal of Integrative Neuroscience . 2006,第3期

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4. Computational Architectural Neuroscience: Towards the Computational Theory of the Human Brain Interactions with Architectural Design [C] . H. Mirkia, A. Sangari, M. Nelson, International Conference on Modeling, Simulation and Visualization Methods . 2012

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7. Innovation in Education: The possible role of dance in driving mathematical competencyCommentary on: Irving, L.T. (2015). Teaching statistics using dance and movement. Frontiers in Psychology, 6(50), doi: 10.3389/fpsyg.2015.00050. and Susac, A., & Braeutigam, S. (2014). A case for neuroscience in mathematics education. Frontiers in Human Neuroscience, 8(314), doi:10.3389/fnhum.2014.00314.Carl SeniorDepartment of Psychology, School of Life & Health Sciences, Aston University, UK. [O] . Carl eSenior 2016

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Computational Neuroscience: Mathematical and Statistical Perspectives

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