The need for reproducible research has become a topic of intense discussion in the neurosciences. Reproducibility is based on building workflows and traceable analysis steps. A key component of such a workflow is the availability of well-tested analysis methods for data processing.
Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages.
The Elephant Project
Elephant is the direct successor to Neurotools and emerged as key innovation of the BrainScaleS EU project. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. It maintains ties to complementary projects such as OpenElectrophy and spykeviewer.
Researchers at INM-6 take an active role in contributing analysis functionality to the Elephant toolbox, and co-lead design, maintenance, documentation and curation of the library together with core members of the Elephant community. In addition to the development of Elephant as a stand-alone tool, the library is being prepared to serve as foundation for the collaborative ICT infrastructure developed by the Human Brain Project under lead of Sonja Grün.
The scope of the library covers analysis methods for diverse topics such as signal processing, spectral analysis, spike train correlation, spike pattern analysis and or spike-triggered averaging. In the context of hypothesis testing, Elephant contains utility modules for the generation of realizations of stochastic processes and of surrogate signals.
Contributions to Elephant
Authors and Contributors (this leads back to the GitHub page. Is it absolutely necessary?)