Wiki source code of Elephant Tutorials

Version 7.2 by denker on 2021/02/01 17:30

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7.2	5	= Elephant Tutorial Space[[image:https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png\|\|alt="Elephant logo" style="float:right"]] =
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7.2	7	Video tutorials on neuronal data analysis using Elephant
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5.1	15	== A resource for kick-starting work with the Elephant library ==
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5.2	17	The Python library [[Electrophysiology Analysis Toolkit (Elephant)>>https://python-elephant.org\|\|rel="noopener noreferrer" target="_blank"]] 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.
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5.1	19	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>>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>>url:https://doi.org/10.1038/sdata.2018.55]]). All video tutorials are approximately 30 minutes in length.
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5.2	22	== Access to the tutorials ==
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5.4	24	To access the tutorials, check out the drive space of this Collab. Video are available for download in the (% style="color:#f39c12" %)videos(%%) section, whereas the corresponding Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder. Notebooks can either be run directly on the EBRAINS Collaboratory (currently limited to HBP affiliated members), or downloaded and run locally. For local execution, please use the provided (% style="color:#f39c12" %)requirements.txt(%%) file to generate an appropriate Python environment.
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7.1	26	=== Execution on the EBRAINS Collaboratory ===
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	28	* Open the EBRAINS lab by selecting the corresponding (% style="color:#f39c12" %)Lab(%%) menu entry on the left.
	29	//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.//
	30	* In the lab, navigate to a particular notebook and open and execute it. To save changes you may want to make, please create a copy of the notebook in a Collab of your own (i.e., where you have write permissions).
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7.1	32	=== Local execution ===
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	34	* Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
	35	* Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
	36	* Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup.
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5.2	38	== List of available tutorials ==
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	42	\|=(% style="width: 300px;" %)Tutorial\|=(% style="width: 267px;" %)Hosts and Authors\|=(% style="width: 626px;" %)Content
7.2	43	\|(% style="width:300px" %)LFP_analysis\|(% style="width:267px" %)Robin Gutzen\|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
	44	\|(% style="width:300px" %)Spike_analysis\|(% style="width:267px" %)(((
5.2	45	Cristiano Köhler
	46	Alexander Kleinjohann
	47	)))\|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
7.2	48	\|(% style="width:300px" %)Spatio-temporal_spike_patterns\|(% style="width:267px" %)Regimatas Jurkus
5.2	49	Alessandra Stella\|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
7.2	50	\|(% style="width:300px" %)GPFA\|(% style="width:267px" %)Simon Essink\|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
	51	\|(% style="width:300px" %)Surrogate_techniques\|(% style="width:267px" %)Peter Bouss\|(% style="width:626px" %)Learn how to use different surrogate methods for spike trains to assist in formulating statistical null hypotheses in the presence of non-stationarity.
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	59	{{box title="Contents"}}
	60	{{toc/}}
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