Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 41.1
edited by denker
on 2022/11/11 12:18
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To version 27.1
edited by denker
on 2021/04/16 21:38
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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" %)Upcoming Sessions:
11 -
12 -(% style="color:#e74c3c" %)6.12.22 Intermediate data analysis in Python: Using Neo and Elephant for
13 -neural activity analysis
10 +(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
14 14  )))
15 15  )))
16 16  
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18 18  (((
19 19  (% class="col-xs-12 col-sm-8" %)
20 20  (((
21 -{{info}}
22 -== Intermediate Data Analysis in Python (Hybrid) ==
23 -
24 -**Using Neo and Elephant for neural activity analysis**
25 -
26 -=== Information ===
27 -
28 -Date: Tuesday, December 6, 2022
29 -
30 -Time: tba
31 -
32 -Registration & Agenda: tba
33 -
34 -
35 -{{/info}}
36 -
37 37  == A resource for kick-starting work with the Elephant library ==
38 38  
39 39  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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40 40  
41 41  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.
42 42  
43 -In addition, tutorials presented at various workshops and schools are collected in this collab.
44 44  
45 -
46 46  == Access to the tutorials ==
47 47  
48 48  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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77 77  |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
78 78  |(% 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.
79 79  
80 -== List of past events
81 - ==
82 -
83 -* (((
84 -November 10, 2022** Simulate with EBRAINS (Online)**
85 -Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
59 +
86 86  )))
87 -)))
88 88  
89 89  
90 -
91 91  (% class="col-xs-12 col-sm-4" %)
92 92  (((
93 93  {{box title="**Contents**"}}
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