[Neuroinfo] First release of the Elephant toolbox for neurophysiology data analysis

Andrew Davison andrew.davison at unic.cnrs-gif.fr
Wed Apr 8 16:07:18 CEST 2015

We are pleased to announce the first release of the Elephant toolbox for the analysis of neurophysiology data - see http://elephant.readthedocs.org/ for more details.

Elephant builds on the Python scientific stack (NumPy, SciPy) to provide a set of well-tested analysis functions for spike train data and time series recordings from electrodes, such as spike train statistics, power spectrum analysis, filtering, cross-correlation and spike-triggered averaging. The toolbox also provides tools to generate artificial spike trains, either from stochastic processes or by controlled perturbations of real spike trains. Elephant is built on the Neo data model, and takes advantage of the broad file-format support provided by the Neo library. A bridge to the Pandas data analysis library is also provided.

Elephant is a community-based effort, aimed at providing a common platform to test and distribute code from different laboratories, with the goal of improving the reproducibility of modern neuroscience research. If you are a neuroscience researcher or student using Python for data analysis, please consider joining us on Github (https://github.com/NeuralEnsemble/elephant), either to contribute your own code or to help with code review and testing.

Elephant is the direct successor to NeuroTools and maintains ties to complementary projects such as OpenElectrophy and SpykeViewer. It is also the default tool for electrophysiology data analysis in the Human Brain Project.

Sonja Grün
Andrew Davison
Michael Denker
Alper Yegenoglu
Detlef Holstein
and all the Elephant contributors.

Dr Andrew Davison
Unité de Neurosciences, Information et Complexité (UNIC)
Centre National de la Recherche Scientifique
1, avenue de la Terrasse
91198 Gif sur Yvette

Tel: +33 1 69 82 34 51

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