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Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 24.1
edited by denker
on 2021/02/03 11:14
Change comment: There is no comment for this version
To version 43.1
edited by denker
on 2022/11/11 12:20
Change comment: There is no comment for this version

Summary

Details

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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 for the HBP Student Conference workshop ~-~-**
10 +
11 +(% style="color:#e74c3c" %)Upcoming Sessions:
12 +
13 +(% style="color:#e74c3c" %)December 6, 2022 Intermediate data analysis in Python: Using Neo and Elephant for
14 +neural activity analysis
11 11  )))
12 12  )))
13 13  
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15 15  (((
16 16  (% class="col-xs-12 col-sm-8" %)
17 17  (((
22 +{{info}}
23 +== Intermediate Data Analysis in Python (Hybrid) ==
24 +
25 +**Using Neo and Elephant for neural activity analysis**
26 +
27 +=== Information ===
28 +
29 +Date: Tuesday, December 6, 2022
30 +
31 +Time: tba
32 +
33 +Registration & Agenda: tba
34 +
35 +
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.
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37 37  === Local execution ===
38 38  
39 39  * Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
40 -* Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
62 +* Download a particular notebook, the datasets, and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
41 41  * Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup.
64 +* Likely, path names to data files must be adjusted accordingly.
42 42  
43 43  == List of available tutorials ==
44 44  
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55 55  |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
56 56  |(% 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.
57 57  
58 -
81 +== List of past events ==
82 +
83 +
84 +* (((
85 +November 10, 2022** Simulate with EBRAINS (Online)**
86 +Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
59 59  )))
88 +)))
60 60  
61 61  
91 +
62 62  (% class="col-xs-12 col-sm-4" %)
63 63  (((
64 64  {{box title="**Contents**"}}
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