Attention: The EBRAINS Drive will be down Friday 21st November, from 18:00 CEST for up to 2 days for maintenance. Please be aware that this will also affect the Lab, as notebooks stored in the Drive will not be available.


Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 44.1
edited by denker
on 2022/11/11 12:20
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To version 25.1
edited by denker
on 2021/02/03 11:35
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Summary

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7 7  (% style="color:#4e5f70" %)Interactive video tutorials on
8 8  neuronal data analysis using Elephant
9 9  
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
10 +(% style="color:#e74c3c" %)**~-~- in beta for the HBP Student Conference workshop ~-~-**
15 15  )))
16 16  )))
17 17  
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19 19  (((
20 20  (% class="col-xs-12 col-sm-8" %)
21 21  (((
22 -{{info}}
23 -(% class="wikigeneratedid wikigeneratedheader" id="HIntermediateDataAnalysisinPython28Hybrid29" %)
24 -**Intermediate Data Analysis in Python (Hybrid)**
25 -
26 -**Session: Using Neo and Elephant for neural activity analysis**
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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