Warning: The Collaboratory Drive will be down Friday 14th June , from 18:00 CET (my timezone) until Monday evening for a migration effort.



Elephant Tutorial SpaceElephant logo

Interactive video tutorials on
neuronal data analysis using Elephant

 

Upcoming training events

 

A resource for kick-starting work with the Elephant library

The Python library Electrophysiology Analysis Toolkit (Elephant) provides tools for the analysis of 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, a framework which 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 provided by the Neo library, is essential for developing interoperable analysis workflows.

In this collaborative space, we provide hands on video tutorials based on Jupyter notebooks that showcase various types of data analysis, from simple to advanced. Most notebooks are based on a common dataset published at https://gin.g-node.org/INT/multielectrode_grasp (for details cf. Brochier et al (2018) Scientific Data 5, 180055. https://doi.org/10.1038/sdata.2018.55). All video tutorials are approximately 30 minutes in length.

In addition, tutorials presented at various workshops and schools are collected in this collab.

Access to the tutorials

To access the tutorials, check out the drive space of this collab. The Jupyter notebooks are available in the notebooks folder, and links to the videos are embedded within each notebook. Notebooks can either be run directly on the EBRAINS Collaboratory's JupyterLab service (currently limited to HBP-affiliated members), or downloaded and run locally. For local execution, please use the provided requirements.txt file to generate an appropriate Python environment.

Execution on the EBRAINS Collaboratory

  • Open the EBRAINS lab by selecting the corresponding Lab menu entry on the left.
    Please note: JupyterLab functionality is currently in beta and not yet available to non-HBP-affiliated Collaboratory users. Please check back in the near future.
  • In the lab, navigate to a particular notebook and open and execute it.
    • Please note that in some instances, you may need to restart the kernel for the notebooks to run (e.g., when new packages are installed by the notebook, or in case of low memory).
    • To save changes you may want to make to a notebook, please create a copy of the notebook in a collab of your own (i.e., a collab where you have write permissions).

Local execution

  • Open the EBRAINS drive by selecting the corresponding Drive menu entry on the left.
  • Download a particular notebook, the datasets, and the requirements.txt to your computer.
  • Create a Python environment based on the requirements.txt file. The details will depend on your particular Python setup.
  • Likely, path names to data files must be adjusted accordingly.

List of available tutorials

TutorialHosts and AuthorsContent
LFP_analysisRobin GutzenApply basic LFP analysis techniques, such as power spectra.
Spike_analysis

Cristiano Köhler
Alexander Kleinjohann

Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
Spatio-temporal_spike_patternsRegimatas Jurkus
Alessandra Stella
Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
GPFASimon EssinkExtract low-dimensional rate trajectories from the population spike activity.
Surrogate_techniquesPeter BoussLearn how to use different surrogate methods for spike trains to assist in formulating statistical null hypotheses in the presence of non-stationarity.
NEST-Elephant

Jessica Mitchell

Moritz Kern

Learn how to simulate a neural network with NEST, analyse data with Elephant and visualize results with Viziphant.

List of past events

July 15, 2023 CNS 2023, 32nd Annual Computational Neuroscience Meeting (Leipzig)
Program: https://www.cnsorg.org/cns-2023-meeting-program

April 5, 2023 Data Analysis using Elephant (Hybrid), SMHB General Assembly
Location: Forschungszentrum Juelich, Germany

November 10, 2022 Simulate with EBRAINS (Online)
Agenda: https://flagship.kip.uni-heidelberg.d/jss/HBPm?m=showAgenda&meetingID=242

July 1, 2022 Satellite tutorial at the annual CNS meeting (Online)
Program: https://ocns.github.io/SoftwareWG/pages/software-wg-satellite-tutorials-at-cns-2022.html

June 13-15, 2022 BASSES workshop (Rome, Italy)
Program: https://www.humanbrainproject.eu/en/education/ebrains- workshops/basses/

 

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