Resources for the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'

Dataset

Title Resources for the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'
Authors Gutzen Robin, von Papen Michael, Trensch Guido, Quaglio Pietro, Grün Sonja, Denker Michael,
Description This repository hosts code and data to reproduce the findings of the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'. In addition, the repository hosts an additional example for the use of the tool "NetworkUnit".
License BSD-3-Clause (https://opensource.org/licenses/BSD-3-Clause)
References Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., and Denker, M. (2018). Reproducible neural network simulations: statistical methods for model validation on the level of network activity data
Trensch, G., Gutzen, R., Blundell, I., Denker, M., and Morrison, A. (2018). Rigorous neural network simulations: a model substantiation methodology for increasing the correctness of simulation results in the absence of experimental validation data
Funding Helmholtz, ZT-I-0003
EU, EU.720270
EU, EU.785907
Keywords Neuroscience
Electrophysiology
Validation
Brain Simulation
Spikes
Data Analysis
Data This dataset can be browsed online here or downloaded as a zip archive (1011 MB). The current version of the dataset repository, possibly with updates, can be found here.
DOI 10.12751/g-node.85d46c
Citation This dataset can be cited as:
Gutzen R, von Papen M, Trensch G, Quaglio P, Grün S, Denker M, (2018) Resources for the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'. G-Node. doi:10.12751/g-node.85d46c
Please also consider citing the material listed in the references
Type Software