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@@ -1,3 +1,20 @@
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-# network_validation
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+# Rigorous and reproducible neural network simulations
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-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.
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+This repository contains the resources (simulation codes, simulation data, analysis codes) for the studies:
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+
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+Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., and Denker, M. (2018).
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+*Reproducible neural network simulations: statistical methods for model validation on the level of network activity data*
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+
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+and
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+
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+Trensch, G., Gutzen, R., Blundell, I., Denker, M., and Morrison, A. (2018).
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+*Rigorous neural network simulations: a model substantiation methodology for increasing the correctness of simulation
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+results in the absence of experimental validation data*
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+
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+The Jupyter Notebook [generate_validation_results](https://web.gin.g-node.org/INM-6/network_validation/src/master/generate_validation_results.ipynb)
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+replicates the reported validation results of Gutzen et al. and guides through the use of the
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+[NetworkUnit](https://github.com/INM-6/NetworkUnit) module.
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+The Jupter Notebook [NetworkUnit_examples](https://web.gin.g-node.org/INM-6/network_validation/src/master/NetworkUnit_examples.ipynb)
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+provides an additional worked example for the NetworkUnit framework by applying it to the quantitative comparison of two experimental data sets.
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+Both notebooks are executable by cloning this repository and installing the necessary packages defined in the requirements file
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+(`pip install -r requirements.txt`).
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