000152183 001__ 152183
000152183 005__ 20240313103118.0
000152183 0247_ $$2doi$$a10.3389/fninf.2014.00023
000152183 0247_ $$2Handle$$a2128/5976
000152183 0247_ $$2WOS$$aWOS:000348106800001
000152183 0247_ $$2Handle$$a2128/11539
000152183 037__ $$aFZJ-2014-01957
000152183 041__ $$aEnglish
000152183 082__ $$a610
000152183 1001_ $$0P:(DE-Juel1)151167$$aZaytsev, Yury$$b0$$eCorresponding Author$$ufzj
000152183 245__ $$aCyNEST: a maintainable Cython-based interface for the NEST simulator
000152183 260__ $$aLausanne$$bFrontiers Research Foundation$$c2014
000152183 3367_ $$2DRIVER$$aarticle
000152183 3367_ $$2DataCite$$aOutput Types/Journal article
000152183 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1563264005_1091
000152183 3367_ $$2BibTeX$$aARTICLE
000152183 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000152183 3367_ $$00$$2EndNote$$aJournal Article
000152183 520__ $$aNEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Originally, simulations were controlled via the Simulation Language Interpreter (SLI), a built-in scripting facility implementing a language derived from PostScript (Adobe Systems, Inc., 1999). The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. As the majority of NEST users found PyNEST easier to use and to combine with other applications, it immediately displaced SLI as the default NEST interface. However, developing and maintaining PyNEST has become increasingly difficult over time. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al., 2011). Code generation via Cython allows the production of smaller and more maintainable bindings, including increased compatibility with all supported Python releases without additional burden for NEST developers. Furthermore, this novel approach opens up the possibility to support alternative implementations of the Python language at no cost given a functional Cython back-end for the corresponding implementation, and also enables cross-compilation of Python bindings for embedded systems and supercomputers alike.
000152183 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
000152183 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x1
000152183 536__ $$0G:(DE-Juel1)HGF-SystemsBiology$$aHASB - Helmholtz Alliance on Systems Biology (HGF-SystemsBiology)$$cHGF-SystemsBiology$$fHASB-2008-2012$$x2
000152183 536__ $$0G:(DE-HGF)B1175.01.12$$aW2Morrison - W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (B1175.01.12)$$cB1175.01.12$$x3
000152183 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x4
000152183 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de
000152183 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b1$$ufzj
000152183 770__ $$aPython in Neuroscience II
000152183 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2014.00023$$gVol. 8$$p23$$tFrontiers in neuroinformatics$$v8$$x1662-5196$$y2014
000152183 8564_ $$uhttps://juser.fz-juelich.de/record/152183/files/FZJ-2014-01957.pdf$$yOpenAccess
000152183 8767_ $$92014-02-22$$d2014-02-24$$eAPC$$jZahlung erfolgt
000152183 909CO $$ooai:juser.fz-juelich.de:152183$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000152183 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151167$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000152183 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000152183 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000152183 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000152183 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
000152183 9141_ $$y2014
000152183 915__ $$0LIC:(DE-HGF)CCBY3$$2HGFVOC$$aCreative Commons Attribution CC BY 3.0
000152183 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000152183 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000152183 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000152183 915__ $$0StatID:(DE-HGF)0020$$2StatID$$aNo Peer Review
000152183 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000152183 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000152183 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000152183 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x2
000152183 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x3
000152183 9801_ $$aAPC
000152183 9801_ $$aFullTexts
000152183 980__ $$ajournal
000152183 980__ $$aVDB
000152183 980__ $$aI:(DE-Juel1)JSC-20090406
000152183 980__ $$aI:(DE-Juel1)IAS-6-20130828
000152183 980__ $$aI:(DE-Juel1)INM-6-20090406
000152183 980__ $$aI:(DE-82)080012_20140620
000152183 980__ $$aAPC
000152183 980__ $$aUNRESTRICTED
000152183 981__ $$aI:(DE-Juel1)IAS-6-20130828
000152183 981__ $$aI:(DE-Juel1)IAS-6-20130828
000152183 981__ $$aI:(DE-Juel1)INM-6-20090406