Software
Resources for the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'
- 1Institute 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
- 2Simulation Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Jülich Research Centre, Jülich, Germany
- 3Institute 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; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
DOI: 10.12751/g-node.85d46c BROWSE REPOSITORY BROWSE ARCHIVE DOWNLOAD ARCHIVE (ZIP 597 MiB)
Published 24 Oct. 2018 | License BSD-3-Clause
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".
Keywords
| Neuroscience | Electrophysiology | Validation | Brain Simulation | Spikes | Data Analysis |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. Front. Neuroinform. 12:90. https://doi.org/10.3389/fninf.2018.00090
- 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. Front. Neuroinform. 12:81. https://doi.org/10.3389/fninf.2018.00081
Funding
- Helmholtz ZT-I-0003
- EU EU.720270
- EU EU.785907