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Author

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1		-XWiki.denker
	1	+XWiki.moritzkern

Content

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7	7	(% style="color:#4e5f70" %)Interactive video tutorials on
8	8	neuronal data analysis using Elephant
9	9
10		-(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
	10	+(% style="color:#e74c3c" %)**Upcoming: CNS 2022**
11	11	)))
12	12	)))
13	13
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15	15	(((
16	16	(% class="col-xs-12 col-sm-8" %)
17	17	(((
	18	+{{info}}
	19	+=== CNS 2022: How to attend? ===
	20	+
	21	+This session is being conducted as part of the [[online satellite tutorials of CNS*2022>>url:https://ocns.github.io/SoftwareWG/pages/software-wg-satellite-tutorials-at-cns-2022.html]].
	22	+
	23	+Date: Friday, Juli 1, 2022
	24	+
	25	+Time: 9:00 to 12:00 CEST
	26	+
	27	+Location: Online (via zoom)
	28	+
	29	+Registration: **Free (but required)!** You can register [[here>>url:https://framaforms.org/incfocns-software-wg-cns2022-satellite-tutorials-registration-1654593600]]!
	30	+
	31	+Please note that to prevent spamming/zoom bombing during the session, __the link to the Zoom meeting will be sent out only to registrants.__
	32	+
	33	+=== Prerequisites ===
	34	+
	35	+There are no prerequisities for attending the session. But an EBRAINS account is required to get access to some of the tools and services that will be demonstrated. If you wish to utilize the 'practice' time to explore these tools by yourself, please create your account at [[https:~~/~~/ebrains.eu/register>>url:https://ebrains.eu/register]] (free of charge).
	36	+{{/info}}
	37	+
18	18	== A resource for kick-starting work with the Elephant library ==
19	19
20	20	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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21	21
22	22	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.
23	23
	44	+In addition, tutorials presented at various workshops and schools are collected in this collab.
24	24
	46	+
25	25	== Access to the tutorials ==
26	26
27	27	To access the tutorials, check out the drive space of this collab. The Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder, and links to the (% style="color:#f39c12" %)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 (% style="color:#f39c12" %)requirements.txt(%%) file to generate an appropriate Python environment.

Collaboratory.Apps.Collab.Code.CollabClass[0]

owner

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	1	+denker