Analyse the neuronal dynamics of experiments and brain simulations with this Electrophysiology Analysis Toolkit.
- Explore functional electrophysiological data from heterogeneous sources in a common analytical framework.
- Reference implementations of advanced analytical methods, using the unifying Neo data model.
- Easy to integrate into applications, including graphical analysis tools, network simulation engines, or databases for electrophysiological data.
- Interactive tutorials to illustrate methodologies for data analysis and open-source community-driven development.
The Python library Electrophysiology Analysis Toolkit (Elephant) provides tools for analysing neuronal activity data, such as spike trains, local field potentials and intracellular data. In addition to providing a platform for sharing analysis codes from different laboratories, Elephant provides a consistent and homogeneous framework for data analysis built on a modular foundation. The underlying data model is the Neo library. This framework easily captures a wide range of neuronal data types and methods, including dozens of file formats and network simulation tools. A common data description, as the Neo library provides, is essential for developing interoperable analysis workflows.
Getting started with Elephant and Neo
Elephant, and its complementary data model Neo, are open-source Python libraries available for use offline. Both come pre-installed on the HBP Collaboratory Jupyter Notebooks service for immediate import. The packages are installable from the Python package index, and the sources are found on GitHub. For details, please take a look at the documentation's install instructions and tutorial sections.
Analysis workflows based on Elephant
Elephant is developed in constant, close exchange with scientific applications to ensure that its functionality will continue to be beneficial for the neuroscience community. A growing number of tutorials assist researchers in setting up their analysis workflows.
Training for the Elephant community
We offer many training opportunities for learning how to use Elephant to analyse activity data. The Advanced Neural Data Analysis spring school (ANDA) teaches students and young researchers how to employ the Elephant and Neo libraries to provide data analytics for electrophysiological data. The Elephant User Workshop complements the ANDA course and gives hands-on support for using Elephant, Neo and related HBP infrastructures in participants’ research projects. In addition, help from the community is always available through the Neural Ensemble mailing list and the EBRAINS support team.