This repository contains the resources (simulation codes, simulation data, analysis codes) for the studies:
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
and
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
The Jupyter Notebook generate_validation_results
replicates the reported validation results of Gutzen et al. and guides through the use of the
NetworkUnit module.
The Jupter Notebook NetworkUnit_examples
provides an additional worked example for the NetworkUnit framework by applying it to the quantitative comparison of two experimental data sets.
Both notebooks are executable by cloning this repository and installing the necessary packages defined in the requirements file
(pip install -r requirements.txt).
