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      NEURON and Python

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          Abstract

          The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

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          Python for Scientific Computing

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            MPI for Python: Performance improvements and MPI-2 extensions

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              Expanding NEURON's repertoire of mechanisms with NMODL.

              Neuronal function involves the interaction of electrical and chemical signals that are distributed in time and space. The mechanisms that generate these signals and regulate their interactions are marked by a rich diversity of properties that precludes a "one size fits all" approach to modeling. This article presents a summary of how the model description language NMODL enables the neuronal simulation environment NEURON to accommodate these differences.
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                Author and article information

                Journal
                Front Neuroinformatics
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Research Foundation
                1662-5196
                21 October 2008
                28 January 2009
                2009
                : 3
                : 1
                Affiliations
                [1] 1Computer Science, Yale University New Haven, CT, USA
                [2] 2Unité de Neurosciences Intégratives et Computationelles, CNRS Gif sur Yvette, France
                [3] 3Laboratory for Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne Switzerland
                Author notes

                Edited by: Rolf Kötter, Radboud University, Nijmegen, The Netherlands

                Reviewed by: Felix Schürmann, Ecole Polytechnique Fédérale de Lausanne, Switzerland; Volker Steuber, University of Hertfordshire, UK; Arnd Roth, University College London, UK

                *Correspondence: Andrew Davison, UNIC, Bât. 32/33, CNRS, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France. e-mail: andrew.davison@ 123456unic.cnrs-gif.fr
                Article
                10.3389/neuro.11.001.2009
                2636686
                19198661
                52cd9854-e53c-4722-9b92-21aa44a037f8
                Copyright © 2009 Hines, Davison and Muller.

                This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

                History
                : 24 September 2008
                : 05 January 2009
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 18, Pages: 12, Words: 7111
                Categories
                Neuroscience
                Original Research

                Neurosciences
                computational neuroscience,simulation environment,python
                Neurosciences
                computational neuroscience, simulation environment, python

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