Changes for page Elephant Tutorials
Last modified by denker on 2025/04/09 07:02
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... ... @@ -7,7 +7,10 @@ 7 7 (% style="color:#4e5f70" %)Interactive video tutorials on 8 8 neuronal data analysis using Elephant 9 9 10 -(% style="color:#e74c3c" %)**Upcoming: CNS 2022** 10 +(% style="color:#e74c3c" %)Upcoming Sessions: 11 + 12 +(% style="color:#e74c3c" %)6.12.22 Intermediate data analysis in Python: Using Neo and Elephant for 13 +neural activity analysis 11 11 ))) 12 12 ))) 13 13 ... ... @@ -15,6 +15,22 @@ 15 15 ((( 16 16 (% class="col-xs-12 col-sm-8" %) 17 17 ((( 21 +{{info}} 22 +== Intermediate Data Analysis in Python (Hybrid) == 23 + 24 +**Using Neo and Elephant for neural activity analysis** 25 + 26 +=== Information === 27 + 28 +Date: Tuesday, December 6, 2022 29 + 30 +Time: tba 31 + 32 +Registration & Agenda: tba 33 + 34 + 35 +{{/info}} 36 + 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. ... ... @@ -58,10 +58,17 @@ 58 58 |(% style="width:300px" %)GPFA|(% style="width:267px" %)Simon Essink|(% style="width:626px" %)Extract low-dimensional rate trajectories from the population spike activity. 59 59 |(% 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. 60 60 61 - 80 +== List of past events 81 + == 82 + 83 +* ((( 84 +November 10, 2022** Simulate with EBRAINS (Online)** 85 +Agenda: https:~/~/flagship.kip.uni-heidelberg.de/jss/HBPm?m=showAgenda&meetingID=242 62 62 ))) 87 +))) 63 63 64 64 90 + 65 65 (% class="col-xs-12 col-sm-4" %) 66 66 ((( 67 67 {{box title="**Contents**"}}