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
Change comment: There is no comment for this version
To version 5.4
edited by denker
on 2021/02/01 17:23
Change comment: There is no comment for this version

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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" style="float:right"]](%%) =
5 += Elephant Tutorial Space =
6 6  
7 -(% style="color:#4e5f70" %)Interactive video tutorials on
8 -neuronal data analysis using Elephant
7 +Video tutorials on data analysis using Elephant
9 9  )))
10 10  )))
11 11  
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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  
22 -
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.
23 +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 (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 -=== Execution on the EBRAINS Collaboratory ===
25 +==== Execution on the EBRAINS Collaboratory ====
28 28  
29 29  * Open the EBRAINS lab by selecting the corresponding (% style="color:#f39c12" %)Lab(%%) menu entry on the left.
30 -//Please note: JupyterLab functionality is currently in beta and not yet available to non-HBP-affiliated Collaboratory users. Please check back in the near future.//
31 -* In the lab, navigate to a particular notebook and open and execute it.
32 -** Please note that in some instances, you may need to restart the kernel for the notebooks to run (e.g., when new packages are installed by the notebook, or in case of low memory).
33 -** To save changes you may want to make to a notebook, please create a copy of the notebook in a collab of your own (i.e., a collab where you have write permissions).
28 +//Please note: Jupyterlab functionality is currently in beta and not yet available to non-HBP-affiliated Collaboratory users. Please check back in the near future.//
29 +* In the lab, navigate to a particular notebook and open and execute it. To save changes you may want to make, please create a copy of the notebook in a Collab of your own (i.e., where you have write permissions).
34 34  
35 -=== Local execution ===
31 +==== Local execution ====
36 36  
37 37  * Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
38 38  * Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
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43 43  
44 44  (% style="margin-right:auto" %)
45 45  |=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content
46 -|(% style="width:300px" %)LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
47 -|(% style="width:300px" %)Spike_analysis|(% style="width:267px" %)(((
42 +|(% style="width:300px" %)Elephant_Tutorial_-_LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
43 +|(% style="width:300px" %)Elephant_Tutorial_-_Spike_analysis|(% style="width:267px" %)(((
48 48  Cristiano Köhler
49 49  Alexander Kleinjohann
50 50  )))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
51 -|(% style="width:300px" %)Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus
47 +|(% style="width:300px" %)Elephant_Tutorial_-_Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus
52 52  Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
53 -|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
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.
49 +|(% style="width:300px" %)Elephant_Tutorial_-_GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
50 +|(% style="width:300px" %)Elephant_Tutorial_-_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.
55 55  
56 56  
57 57  )))
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