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2	2	(((
3	3	(% class="container" %)
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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:#f1c40f" %)Interactive video tutorials on
8		-neuronal data analysis using Elephant
	7	+Video tutorials on data analysis using Elephant
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14	14	(% class="col-xs-12 col-sm-8" %)
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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	)))
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