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7	7	(% style="color:#4e5f70" %)Interactive video tutorials on
8	8	neuronal data analysis using Elephant
9	9
10		-(% style="color:#e74c3c" %)**~-~- in beta  ~-~-**
	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
11	11	)))
12	12	)))
13	13
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15	15	(((
16	16	(% class="col-xs-12 col-sm-8" %)
17	17	(((
	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	+
18	18	== A resource for kick-starting work with the Elephant library ==
19	19
20	20	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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21	21
22	22	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.
23	23
	44	+In addition, tutorials presented at various workshops and schools are collected in this collab.
24	24
	46	+
25	25	== Access to the tutorials ==
26	26
27	27	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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56	56	|(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity.
57	57	|(% 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.
58	58
59		-
	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
60	60	)))
	89	+)))
61	61
62	62
	92	+
63	63	(% class="col-xs-12 col-sm-4" %)
64	64	(((
65	65	{{box title="**Contents**"}}

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

owner

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	1	+denker