Changes for page Elephant Tutorials

Last modified by denker on 2025/04/09 07:02
From version 13.1
edited by denker
on 2021/02/01 18:27
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To version 4.2
edited by denker
on 2021/02/01 16:57
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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13 13  (((
14 14  (% class="col-xs-12 col-sm-8" %)
15 15  (((
16 -== A resource for kick-starting work with the Elephant library ==
15 += The Elephant and =
17 17  
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.
17 +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.
19 19  
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.
19 +In this space, we provide hands on video tutorials based on Jupyter notebooks on performing various types of data analysis based on the 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]]).
21 21  
21 += Who has access? =
22 22  
23 -== Access to the tutorials ==
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 (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 -
27 -=== Execution on the EBRAINS Collaboratory ===
28 -
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. 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).
32 -
33 -=== Local execution ===
34 -
35 -* Open the EBRAINS drive by selecting the corresponding (% style="color:#f39c12" %)Drive(%%) menu entry on the left.
36 -* Download a particular notebook and the (% style="color:#f39c12" %)requirements.txt(%%) to your computer.
37 -* Create a Python environment based on the (% style="color:#f39c12" %)requirements.txt(%%) file. The details will depend on your particular Python setup.
38 -
39 -== List of available tutorials ==
40 -
41 -
42 -(% style="margin-right:auto" %)
43 -|=(% style="width: 300px;" %)Tutorial|=(% style="width: 267px;" %)Hosts and Authors|=(% style="width: 626px;" %)Content
44 -|(% style="width:300px" %)LFP_analysis|(% style="width:267px" %)Robin Gutzen|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
45 -|(% style="width:300px" %)Spike_analysis|(% style="width:267px" %)(((
46 -Cristiano Köhler
47 -Alexander Kleinjohann
48 -)))|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
49 -|(% style="width:300px" %)Spatio-temporal_spike_patterns|(% style="width:267px" %)Regimatas Jurkus
50 -Alessandra Stella|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
51 -|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
52 -|(% 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.
53 -
54 -
23 +Describe the audience of this collab.
55 55  )))
56 56  
57 57  
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