Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 14.1
edited by denker
on 2021/02/01 18:42
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To version 52.1
edited by moritzkern
on 2023/06/27 10:37
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6 6  
7 7  (% style="color:#4e5f70" %)Interactive video tutorials on
8 8  neuronal data analysis using Elephant
9 +
10 +
9 9  )))
10 10  )))
11 11  
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13 13  (((
14 14  (% class="col-xs-12 col-sm-8" %)
15 15  (((
18 +== Upcoming training events ==
19 +
20 +{{info}}
21 +July 15, 2022 CNS 2023, 32nd Annual Computational Neuroscience Meeting (Leipzig)
22 +Program: [[https:~~/~~/www.cnsorg.org/cns-2023-meeting-program>>https://www.cnsorg.org/cns-2023-meeting-program]]
23 +{{/info}}
24 +
16 16  == A resource for kick-starting work with the Elephant library ==
17 17  
18 18  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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19 19  
20 20  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.
21 21  
31 +In addition, tutorials presented at various workshops and schools are collected in this collab.
22 22  
33 +
23 23  == Access to the tutorials ==
24 24  
25 -To access the tutorials, check out the drive space of this collab. Video are available for download in the (% style="color:#f39c12" %)videos(%%) section, whereas the corresponding Jupyter notebooks are available in the (% style="color:#f39c12" %)notebooks(%%) folder. 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.
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.
26 26  
27 27  === Execution on the EBRAINS Collaboratory ===
28 28  
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35 35  === Local execution ===
36 36  
37 37  * Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
38 -* Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
49 +* Download a particular notebook, the datasets, and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
39 39  * Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup.
51 +* Likely, path names to data files must be adjusted accordingly.
40 40  
41 41  == List of available tutorials ==
42 42  
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52 52  Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
53 53  |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
54 54  |(% 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
55 55  
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 +April 5, 2023 **Data Analysis using Elephant (Hybrid), SMHB General Assembly**
77 +Location: Forschungszentrum Juelich, Germany
78 +
79 +
80 +(((
81 +November 10, 2022** Simulate with EBRAINS (Online)**
82 +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]]
83 +
84 +
85 +July 1, 2022 **Satellite tutorial at the annual CNS meeting (Online)**
86 +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]]
87 +
88 +
89 +June 13-15, 2022 **BASSES workshop (Rome, Italy)**
90 +Program: [[https:~~/~~/www.humanbrainproject.eu/en/education/ebrains- workshops/basses/>>https://www.humanbrainproject.eu/en/education/ebrains-workshops/basses/]]
91 +
92 +
56 56  
57 57  )))
95 +)))
58 58  
59 -
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61 61  (((
62 62  {{box title="**Contents**"}}
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