Simulation data (implementations in C and on the SpiNNaker neuromorphic architecture) and a Jupyter notebook replicating the results presented in Trensch et al. and Gutzen et al. using the NetworkUnit package.

Michael Denker b154bbd44d Update 'datacite.yml' 9 months ago
simulation_code 2a804de26b updating simulations codes from github.com/gtrensch/ 9 months ago
simulation_data 1fcba709ac replace corrupted spikefile (itIII/SpiNN/after2h) 10 months ago
spade_analysis a4ec7d91b6 Initial commit adding data, analysis scripts and results 1 year ago
.gitignore a4ec7d91b6 Initial commit adding data, analysis scripts and results 1 year ago
LICENSE 0f6b621bec Update 'LICENSE' 9 months ago
NetworkUnit_examples.ipynb aaa16ab074 align intros of notebooks 9 months ago
README.md dc7a438763 expand readme 9 months ago
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generate_validation_results.ipynb 009ff1fca9 remove verbose output for readablity 9 months ago
requirements.txt 34d167b0b6 notebook ready for Python3, requires numpy==1.15.3 9 months ago

README.md

Rigorous and reproducible neural network simulations

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).

Datacite.yml
Title Resources for the article 'Reproducible neural network simulations: statistical methods for model validation on the level of network activity data'
Authors Gutzen,Robin;Institute 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;0000-0001-7373-5962
von Papen,Michael;Institute 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;0000-0001-5030-1643
Trensch,Guido;Simulation Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Jülich Research Centre, Jülich, Germany;0000-0003-0411-3726
Quaglio,Pietro;Institute 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;0000-0002-8012-3056
Grün,Sonja;Institute 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;0000-0003-2829-2220
Denker,Michael;Institute 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;0000-0003-1255-7300
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 [] (IsSupplementTo)
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 [] (References)
Funding Helmholtz, ZT-I-0003
EU, EU.720270
EU, EU.785907
Keywords Neuroscience
Electrophysiology
Validation
Brain Simulation
Spikes
Data Analysis
Resource Type Dataset