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Created with Raphaël 2.2.017Mar16Jan1527May22All MEA features of all experiments. mainmainfixed indentfixed indentadded explanation of new functionparameters used for simulations with a mask of valid sims appliedparameters used for simulationsNDE trained on the MEA features from the 100.000 simulations the embedding network+NDE was also trained on NDE and embedding net trained together on spike train dataadding trained embedding network with NDE and the spiketrains used to train itScript to perform PPC and calculate PRE of different trained NDEsScript to test for model misspecificationScript showing how NDE was trained in the manuscript with either 15 MEA features or using an embedding network.Splitted Simulator.py into simulator and this FeatureExtraction.py that will also be called from TrainNDE script. Splitting Simulator.py into a simulator and feature extraction scriptAdd LICENSEUpdate FindPosteriors.pychanges reference from CACNA1A preprint to publicationFixed MEA feature namescorrected saving location errorIncluded all example observationsUpdate README.mdDelete SCN_GEFS_2410.ptDelete SCN_DS_2410.ptUpload New FileUpload New FileUpload New Filedirectory with some example observations that can be used to try out the codeUpdate README.mdExplanation of Gitlab directoryUpload New Fileexample experimental observation 2example experimental observation 1Update FindPosteriors.pycleaned Makefigures.pyAdded code to make conditional correlations and perform KS statistical test Update README.mdpython code to evaluate experimental data and create the posterior distributions Python functions to create the Figures used in the paper. Python functions to simulate the neuronal network on MEA with certain parameters and too extract the MEA features from simulations or experiment. The output features of the 300,000 simulations used to train the NDE