000892654 001__ 892654
000892654 005__ 20250321120424.0
000892654 037__ $$aFZJ-2021-02240
000892654 041__ $$aEnglish
000892654 1001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b0$$eCorresponding author
000892654 1112_ $$aNeuroFrance 2021$$cOnline$$d2021-05-19 - 2021-05-21$$wOnline
000892654 245__ $$aOrchestrating analysis workflows using Elephant and Neo
000892654 260__ $$c2021
000892654 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1636121450_6647
000892654 3367_ $$033$$2EndNote$$aConference Paper
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000892654 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000892654 3367_ $$2ORCID$$aOTHER
000892654 520__ $$aIn order to deal with the increasing complexity of data from electrophysiological experiments and spiking neural network simulations, concepts and tools to perform data acquisition and analysis in a reproducible fashion are in high demand. Here, following [1], we demonstrate open-source software solutions that support such workflows, each addressing different aspects of the process: (i) electrophysiological data of different origins are represented in a standard description using Neo (RRID:SCR_000634) [2], (ii) complex metadata accumulating in the electrophysiological experiment [3] are organized [4] using the open metadata markup language (odML, RRID:SCR_001376) [5], and (iii) analysis is performed using the Electrophysiology Analysis Toolkit (Elephant, RRID:SCR_003833, http://python-elephant). Elephant acts as the central modular software component that provides generic library functions to perform standard and advanced analysis methods for parallel, multi-scale activity data. We outline how the integration of such workflows into the EBRAINS infrastructure facilitates interdisciplinary, collaborative work including access to high-performance computing. In particular, we demonstrate how such tools form the basis for rigorous approaches to model validation [6].
000892654 536__ $$0G:(DE-HGF)POF4-523$$a523 - Neuromorphic Computing and Network Dynamics (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000892654 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000892654 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000892654 536__ $$0G:(DE-HGF)ZT-I-0003$$aHAF - Helmholtz Analytics Framework (ZT-I-0003)$$cZT-I-0003$$x3
000892654 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x4
000892654 536__ $$0G:(DE-HGF)HMC_20200306$$aHMC - Helmholz Metadata Collaboration ((DE-HGF)HMC_20200306)$$c(DE-HGF)HMC_20200306$$x5
000892654 588__ $$aDataset connected to DataCite
000892654 8564_ $$uhttps://www.professionalabstracts.com/nf2021/iplanner/#/presentation/275
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000892654 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144807$$aForschungszentrum Jülich$$b0$$kFZJ
000892654 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000892654 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000892654 9130_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000892654 9141_ $$y2021
000892654 920__ $$lno
000892654 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000892654 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000892654 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000892654 980__ $$aabstract
000892654 980__ $$aVDB
000892654 980__ $$aI:(DE-Juel1)INM-6-20090406
000892654 980__ $$aI:(DE-Juel1)INM-10-20170113
000892654 980__ $$aI:(DE-Juel1)IAS-6-20130828
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000892654 981__ $$aI:(DE-Juel1)IAS-6-20130828