Summary

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Author

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1		-XWiki.denker
	1	+XWiki.moritzkern

Content

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2	2	(((
3	3	(% class="container" %)
4	4	(((
5		-= (% style="color:#f39c12" %)Elephant Tutorial Space[[image:https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png||alt="Elephant logo" style="float:right"]](%%) =
	5	+= (% style="color:#f39c12" %)Elephant Tutorial Space[[image:https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png||alt="Elephant logo" data-xwiki-image-style-alignment="end"]](%%) =
6	6
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	+
11	11	)))
12	12	)))
13	13
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15	15	(((
16	16	(% class="col-xs-12 col-sm-8" %)
17	17	(((
	18	+== Upcoming training events ==
	19	+
	20	+{{info}}
	21	+March 12, 2025 **Accelerate Your Neuroscience Research with EBRAINS** **(Heidelberg, Germany)**
	22	+Program: [[https:~~/~~/www.ebrains.eu/news-and-events/tutorials-and-users-day-2025>>https://www.ebrains.eu/news-and-events/tutorials-and-users-day-2025]]
	23	+{{/info}}
	24	+
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
	31	+In addition, tutorials presented at various workshops and schools are collected in this collab.
24	24
	33	+
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.
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55	55	Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
56	56	|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
57	57	|(% 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.
	67	+|(% style="width:300px" %)NEST-Elephant|(% style="width:267px" %)(((
	68	+Jessica Mitchell
58	58
	70	+Moritz Kern
	71	+)))|(% style="width:626px" %)Learn how to simulate a neural network with NEST, analyse data with Elephant and visualize results with Viziphant.
	72	+
	73	+== List of past events ==
	74	+
	75	+
	76	+July 15, 2023 **CNS 2023, 32nd Annual Computational Neuroscience Meeting** (Leipzig)
	77	+Program: [[https:~~/~~/www.cnsorg.org/cns-2023-meeting-program>>https://www.cnsorg.org/cns-2023-meeting-program]]
	78	+
	79	+
	80	+April 5, 2023 **Data Analysis using Elephant (Hybrid), SMHB General Assembly**
	81	+Location: Forschungszentrum Juelich, Germany
	82	+
	83	+
	84	+(((
	85	+November 10, 2022** Simulate with EBRAINS (Online)**
	86	+Agenda: [[https:~~/~~/flagship.kip.uni-heidelberg.d/jss/HBPm?m=showAgenda&meetingID=242>>https://flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242]]
	87	+
	88	+
	89	+July 1, 2022 **Satellite tutorial at the annual CNS meeting (Online)**
	90	+Program: [[https:~~/~~/ocns.github.io/SoftwareWG/pages/software-wg-satellite-tutorials-at-cns-2022.html>>https://ocns.github.io/SoftwareWG/pages/software-wg-satellite-tutorials-at-cns-2022.html]]
	91	+
	92	+
	93	+June 13-15, 2022 **BASSES workshop (Rome, Italy)**
	94	+Program: [[https:~~/~~/www.humanbrainproject.eu/en/education/ebrains- workshops/basses/>>https://www.humanbrainproject.eu/en/education/ebrains-workshops/basses/]]
	95	+
	96	+
59	59
60	60	)))
	99	+)))
61	61
62		-
63	63	(% class="col-xs-12 col-sm-4" %)
64	64	(((
65	65	{{box title="**Contents**"}}

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

owner

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	1	+denker