Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 55.1
edited by moritzkern
on 2025/02/18 14:22
Change comment: There is no comment for this version
To version 29.1
edited by denker
on 2022/03/22 18:06
Change comment: Migrated property [owner] from class [Collaboratory.Apps.Collab.Code.CollabClass]

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Author
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1 -XWiki.moritzkern
1 +XWiki.denker
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" data-xwiki-image-style-alignment="end"]](%%) =
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"]](%%) =
6 6  
7 7  (% style="color:#4e5f70" %)Interactive video tutorials on
8 8  neuronal data analysis using Elephant
9 9  
10 -
10 +(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
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 -
25 25  == A resource for kick-starting work with the Elephant library ==
26 26  
27 27  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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28 28  
29 29  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.
30 30  
31 -In addition, tutorials presented at various workshops and schools are collected in this collab.
32 32  
33 -
34 34  == Access to the tutorials ==
35 35  
36 36  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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64 64  Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
65 65  |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
66 66  |(% 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
69 69  
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 -
97 97  
98 98  )))
99 -)))
100 100  
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101 101  (% class="col-xs-12 col-sm-4" %)
102 102  (((
103 103  {{box title="**Contents**"}}
Collaboratory.Apps.Collab.Code.CollabClass[0]
owner
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1 -denker