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Robin Gutzen 5 years ago
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README.md

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-# network_validation
+# Rigorous and reproducible neural network simulations
 
-Simulation data (implementations in C and on the SpiNNaker neuromorphic architecture) and a Jupyter notebook replicating the results presented in submitted manuscripts Trensch et al. and Gutzen et al,  using the NetworkUnit package.
+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](https://web.gin.g-node.org/INM-6/network_validation/src/master/generate_validation_results.ipynb)
+replicates the reported validation results of Gutzen et al. and guides through the use of the 
+[NetworkUnit](https://github.com/INM-6/NetworkUnit) module.  
+The Jupter Notebook [NetworkUnit_examples](https://web.gin.g-node.org/INM-6/network_validation/src/master/NetworkUnit_examples.ipynb)
+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`).