This title appears in the Scientific Report :
2021
Orchestrating analysis workflows using Elephant and Neo.
Orchestrating analysis workflows using Elephant and Neo.
In 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 sol...
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Personal Name(s): | Denker, Michael (Corresponding author) |
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Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2021
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Conference: | NeuroFrance 2021, Online (Online), 2021-05-19 - 2021-05-21 |
Document Type: |
Conference Presentation |
Research Program: |
Helmholtz Analytics Framework Human Brain Project Specific Grant Agreement 3 Theory, modelling and simulation Neuroscientific Foundations Digitization of Neuroscience and User-Community Building Helmholz Metadata Collaboration Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) |
Publikationsportal JuSER |
In 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]. |