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.

Robin cacd425b74 git-annex in rgutzen@PC0W9KFR:~/Projects/network_validation 3 months ago
simulation_code 2a804de26b updating simulations codes from github.com/gtrensch/ 3 years ago
simulation_data 1fcba709ac replace corrupted spikefile (itIII/SpiNN/after2h) 3 years ago
spade_analysis a4ec7d91b6 Initial commit adding data, analysis scripts and results 3 years ago
.gitignore a4ec7d91b6 Initial commit adding data, analysis scripts and results 3 years ago
LICENSE 0f6b621bec Update 'LICENSE' 3 years ago
NetworkUnit_examples.ipynb aaa16ab074 align intros of notebooks 3 years ago
README.md dc7a438763 expand readme 3 years ago
datacite.yml b154bbd44d Update 'datacite.yml' 3 years ago
generate_validation_results.ipynb cacd425b74 git-annex in rgutzen@PC0W9KFR:~/Projects/network_validation 3 months ago
requirements.txt 34d167b0b6 notebook ready for Python3, requires numpy==1.15.3 3 years 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