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@ARTICLE{Muller:204970,
      author       = {Muller, Eilif and Bednar, James A. and Diesmann, Markus and
                      Gewaltig, Marc-Oliver and Hines, Michael and Davison, Andrew
                      P.},
      title        = {{P}ython in neuroscience},
      journal      = {Frontiers in neuroinformatics},
      volume       = {9},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2015-05501},
      pages        = {11},
      year         = {2015},
      abstract     = {This Research Topic of Frontiers in Neuroinformatics is
                      dedicated to the memory of Rolf Kötter (1961–2010), who
                      was the Frontiers Associate Editor responsible for this
                      Research Topic, and who gave us considerable support and
                      encouragement during the process of conceiving and launching
                      the Topic, and throughout the reviewing process.Computation
                      is becoming essential across all sciences, for data
                      acquisition and analysis, automation, and hypothesis testing
                      via modeling and simulation. As a consequence, software
                      development is becoming a critical scientific activity.
                      Training of scientists in programming, software development,
                      and computational thinking (Wilson, 2006), choice of tools,
                      community-building and interoperability are all issues that
                      should be addressed, if we wish to accelerate scientific
                      progress while maintaining standards of correctness and
                      reproducibility.The Python programming language in
                      particular has seen a surge in popularity across the
                      sciences, for reasons which include its readability,
                      modularity, and large standard library. The use of Python as
                      a scientific programming language began to increase with the
                      development of numerical libraries for optimized operations
                      on large arrays in the late 1990s, in which an important
                      development was the merging of the competing Numeric and
                      Numarray packages in 2006 to form NumPy (Oliphant, 2007). As
                      Python and NumPy have gained traction in a given scientific
                      domain, we have seen the emergence of domain-specific
                      ecosystems of open-source Python software developed by
                      scientists. It became clear to us in 2007 that we were on
                      the cusp of an emerging Python in neuroscience ecosystem,
                      particularly in computational neuroscience and neuroimaging,
                      but also in electrophysiological data analysis and in
                      psychophysics.Two major strengths of Python are its
                      modularity and ability to easily “glue” together
                      different programming languages, which together facilitate
                      the interaction of modular components and their composition
                      into larger systems. This focus on reusable components,
                      which has proven its value in commercial and open-source
                      software development (Brooks, 1987), is, we contend,
                      essential for scientific computing in neuroscience, if we
                      are to cope with the increasingly large amounts of data
                      being produced in experimental labs, and if we wish to
                      understand and model the brain in all its complexity.},
      cin          = {IAS-6 / INM-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-6-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 573 -
                      Neuroimaging (POF3-573) / 571 - Connectivity and Activity
                      (POF3-571)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-573 /
                      G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000370604400001},
      pubmed       = {pmid:25926788},
      doi          = {10.3389/fninf.2015.00011},
      url          = {https://juser.fz-juelich.de/record/204970},
}