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@INPROCEEDINGS{Yegenoglu:255984,
      author       = {Yegenoglu, Alper and Denker, Michael and Grün, Sonja and
                      Phan, Long Duc and Davison, Andrew and Holstein, Detlef},
      title        = {{E}lephant – {O}pen-{S}ource {T}ool for the {A}nalysis of
                      {E}lectrophysiological {D}ata {S}ets},
      reportid     = {FZJ-2015-06042},
      pages        = {26},
      year         = {2015},
      abstract     = {The need for reproducible research has become a topic of
                      intense discussion in the neurosciences. Reproducibility is
                      based on building well-defined workflows leading to
                      documented, traceable analysis steps. In recent years
                      software tools (e.g., Neurotools [1], spykeutils [2],
                      OpenElectrophy [3]) have been developed to analyze
                      electrophysiological data. However, many tools tend to
                      specialize in particular types of analysis and do not use a
                      common data model, forcing the user to rely on multiple
                      tools during an analysis. Often the code base of such tools
                      is not written in a modular way, which complicates the
                      combination and comparison of different analysis
                      methods.Here we introduce the Electrophysiology Analysis
                      Toolkit (Elephant) as a community-centered initiative
                      (http://neuralensemble.org/elephant/). Elephant is an
                      easy-to-use, open source Python toolkit, that offers a broad
                      range of functions for analyzing multi-scale data of brain
                      dynamics from experiments and brain simulations. The focus
                      is the analysis of electrical activity, ranging from single
                      unit or massively parallel spike train data to population
                      signals such as the local field potentials. The scope of the
                      library covers analysis methods for time series data (e.g.,
                      signal processing, spectral analysis), spike trains (e.g.,
                      spike train correlation, spike pattern analysis) and methods
                      for relating both signal types (e.g., spike-triggered
                      averaging). In the context of hypothesis testing, utility
                      modules for the generation of realizations of stochastic
                      processes and of surrogate signals are implemented.We chose
                      to use Neo [4] as the underlying data model. This guarantees
                      compatibility within the toolkit, but also provides access
                      to various file I/O modules to access data in both open and
                      proprietary formats. We demonstrate the usage of Elephant in
                      the form of use cases, and outline how to parallelize
                      analyses using the toolkit. In particular, we illustrate the
                      use of Elephant and the task-system on the Unified Portal
                      (UP) [5] of the Human Brain Project which will be the
                      central platform for collaboration by managing complex
                      analysis workflows in a provenance-tracked fashion. Using
                      the web interface of the UP, neuroscientists can launch
                      either generic analysis scripts made available to the
                      community to analyze their data, or alternatively upload and
                      run custom-tailored analysis programs based on Neo and
                      Elephant. The collaborative nature of the portal will enable
                      scientists to easily share and reproduce an analysis inside
                      or even outside their collaborative groups on the UP.
                      Elephant is released on the python package index PyPI [6],
                      and documentation is available at [7]. Please feel free to
                      contribute your analysis tools into Elephant![1]
                      http://neuralensemble.org/NeuroTools/[2]
                      http://spykeutils.readthedocs.org/en/0.4.1/[3]
                      http://neuralensemble.org/OpenElectrophy/[4] Garcia et al.
                      (2014) Front. Neuroinform 8:10,
                      doi:10.3389/fninf.2014.00010[5]
                      $https://developer.humanbrainproject.eu/docs/Unified\%20Portal/latest/[6]$
                      https://pypi.python.org/pypi/elephant[7]
                      http://elephant.readthedocs.org/en/latest/index.htm},
      month         = {Sep},
      date          = {2015-09-17},
      organization  = {INM Retreat 2015, Juelich (Germany),
                       17 Sep 2015 - 18 Sep 2015},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP - The
                      Human Brain Project (604102) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) /
                      BRAINSCALES - Brain-inspired multiscale computation in
                      neuromorphic hybrid systems (269921)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)604102 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)269921},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/255984},
}