This title appears in the Scientific Report :
2015
Elephant – Open-Source Tool for the Analysis of Electrophysiological Data Sets
Elephant – Open-Source Tool for the Analysis of Electrophysiological Data Sets
The need for reproducible research has become a topic of intense discussion in theneurosciences. Reproducibility is based on building workflows and traceable analysissteps. In recent years software tools (e.g., Neurotools [1], spykeutils [2], OpenElectrophy[3]) have been developed to analyze electro...
| Personal Name(s): | Yegenoglu, Alper (Corresponding author) |
|---|---|
| Denker, Michael / Grün, Sonja / Davison, Andrew / Detlef, Holstein / Phan, Long Duc / Chorley, Paul / Ito, Junji / Jennings, Todd / Meyes, Richard / Quaglio, Pietro / Rostami, Vahid / Sprenger, Julia / Torre, Emiliano | |
| Contributing Institute: |
Theoretical Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
| Imprint: |
2015
|
| Physical Description: |
134-135 |
| Conference: | Bernstein Conference 2015, Heidelberg (Germany), 2015-09-14 - 2015-09-16 |
| Document Type: |
Contribution to a conference proceedings |
| Research Program: |
Supercomputing and Modelling for the Human Brain Brain-inspired multiscale computation in neuromorphic hybrid systems The Human Brain Project Connectivity and Activity |
| Publikationsportal JuSER |
|
The need for reproducible research has become a topic of intense discussion in theneurosciences. Reproducibility is based on building workflows and traceable analysissteps. In recent years software tools (e.g., Neurotools [1], spykeutils [2], OpenElectrophy[3]) have been developed to analyze electrophysiological data. However, many toolstend 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 ofsuch tools is not written in a modular way, which complicates the combination andcomparison 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 library, that offers a broad range of functions for analyzing multi-scale data of brain dynamics from experiments and brain simulations. The focus is theanalysis of electrical activity, such as single unit or massively parallel spike train data andlocal field potentials (LFP). The scope of the library covers signal-based analysis (e.g.,signal processing, spectral analysis), spike-based analysis (e.g., spike train correlation,spike pattern analysis) and methods combining both signal types (e.g., spike-triggeredaveraging). In the context of hypothesis testing, utility modules for the generation ofrealizations of stochastic processes and of surrogate signals are implemented.We chose to use Neo [4] as the underlying data model. This guarantees compatibilitywithin the toolkit, but also provides access to various file I/O modules to access data inboth open and proprietary formats. We demonstrate the usage of Elephant in the formof use cases, and outline how to parallelize analyses within the library. In particular, weillustrate the use of Elephant within the Human Brain Project framework.References1 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 |