Last modified by jfioril1 on 2022/05/23 22:36
From version 77.1
edited by denker
on 2020/05/08 15:32
Change comment: There is no comment for this version
To version 74.1
edited by denker
on 2020/05/07 14:22
Change comment: There is no comment for this version

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4 4  (((
5 5  = Temporal Structure in Recorded Ensemble Activity =
6 6  
7 -**Contribution to Use Case SGA2-SP3-003: Pipeline for analyzing the temporal structure in recorded Ensemble Activity and related Systems Neuroscience data in association with behavior, cognitive processing and modeling**
7 +**Contribution to Use Case SGA2-SP3-003: Pipeline for analyzing Temporal structure in recorded Ensemble Activity and related Systems Neuroscience data in association with behavior, cognitive processing and modeling**
8 8  
9 9  Experiments: Julien Fiorilli^^2^^
10 10  
11 11  Implementation: Regimantas Jurkus^^1^^, Julien Fiorilli^^2^^, Pietro Marchesi^^2^^, Thijs Ruikes^^2^^
12 12  
13 -Lead: Michael Denker^^1^^, Sonja Grün^^1,3^^, Cyriel Pennartz^^2^^
13 +Lead: Michael Denker^^1^^, Sonja Grün^^1^^, Cyriel Pennartz^^2^^
14 14  
15 15  ,,1) Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany,,
16 16  
17 17  ,,2) Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands,,
18 -
19 -,,3) Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany,,
20 20  )))
21 21  )))
22 22  
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26 26  (((
27 27  == Synopsis of this demonstration ==
28 28  
29 -This notebook, aimed at experimental and computational neuroscientists, demonstrates the principles of performing analysis of activity data on a complex dataset using the Neo and Elephant tools. We demonstrate the advantage of a common and shared data representation format for a challenging and complex electrophysiology dataset. Also, we showcase the first part of a pipeline for analysing for population-level data, including parallel spiking activity, paired with behavioural and cognitive state variables.
27 +This notebook, aimed at experimental and computational neuroscientists, demonstrates the principles of performing analysis of activity data on a complex dataset using the Neo and Elephant tools. We demonstrate the advantage of a common and shared data representation format for a challenging and complex electrophysiology dataset. Also, we showcase the first part of a pipeline for analysing for population-level data, including parallel spiking activity, paired with and behavioural and cognitive state variables.
30 30  
31 31  A detailed description is found in the interactive Jupyter notebook.
32 32