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7	7	(% style="color:#4e5f70" %)Interactive video tutorials on
8	8	neuronal data analysis using Elephant
9	9
10		-
11		-(% style="color:#e74c3c" %)Upcoming Sessions:
12		-
13		-(% style="color:#e74c3c" %)December 6, 2022 Intermediate data analysis in Python: Using Neo and Elephant for
14		-neural activity analysis
	10	+(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
15	15	)))
16	16	)))
17	17
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19	19	(((
20	20	(% class="col-xs-12 col-sm-8" %)
21	21	(((
22		-{{info}}
23		-== Intermediate Data Analysis in Python (Hybrid) ==
24		-
25		-**Using Neo and Elephant for neural activity analysis**
26		-
27		-=== Information ===
28		-
29		-Date: Tuesday, December 6, 2022
30		-
31		-Time: tba
32		-
33		-Registration & Agenda: tba
34		-
35		-
36		-{{/info}}
37		-
38	38	== A resource for kick-starting work with the Elephant library ==
39	39
40	40	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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41	41
42	42	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.
43	43
44		-In addition, tutorials presented at various workshops and schools are collected in this collab.
45	45
46		-
47	47	== Access to the tutorials ==
48	48
49	49	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.
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78	78	|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
79	79	|(% 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.
80	80
81		-== List of past events ==
82		-
83		-== ==
84		-
85		-* (((
86		-November 10, 2022** Simulate with EBRAINS (Online)**
87		-Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242
	59	+
88	88	)))
89		-)))
90	90
91	91
92		-
93	93	(% class="col-xs-12 col-sm-4" %)
94	94	(((
95	95	{{box title="**Contents**"}}

Collaboratory.Apps.Collab.Code.CollabClass[0]

owner

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1		-denker