Wiki source code of Elephant Tutorials

Version 5.3 by denker on 2021/02/01 17:15

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4.2	5	= Elephant Tutorial Space =
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4.2	7	Video tutorials on data analysis using Elephant
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5.1	15	== A resource for kick-starting work with the Elephant library ==
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5.2	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.
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5.1	19	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.
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5.2	21	== Access to the tutorials ==
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5.1	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 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.
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	25	== List of available tutorials ==
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	29	\|=(% style="width: 300px;" %)Tutorial\|=(% style="width: 267px;" %)Hosts and Authors\|=(% style="width: 626px;" %)Content
	30	\|(% style="width:300px" %)Elephant_Tutorial_-_LFP_analysis\|(% style="width:267px" %)Robin Gutzen\|(% style="width:626px" %)Apply basic LFP analysis techniques, such as power spectra.
	31	\|(% style="width:300px" %)Elephant_Tutorial_-_Spike_analysis\|(% style="width:267px" %)(((
	32	Cristiano Köhler
	33	Alexander Kleinjohann
	34	)))\|(% style="width:626px" %)Perform basic statistical analysis of spike trains from rate profiles to pair-wise correlations.
	35	\|(% style="width:300px" %)Elephant_Tutorial_-_Spatio-temporal_spike_patterns\|(% style="width:267px" %)Regimatas Jurkus
	36	Alessandra Stella\|(% style="width:626px" %)Highlights two methods for detecting hidden spatio-temporal patterns in spike data.
	37	\|(% style="width:300px" %)Elephant_Tutorial_-_GPFA\|(% style="width:267px" %)Simon Essink\|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
	38	\|(% 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.
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	46	{{box title="Contents"}}
	47	{{toc/}}
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