All code uses the mackelab sbi package (https://github.com/sbi-dev/sbi) in a python 3.9 environment.
All code uses the mackelab sbi package (https://github.com/sbi-dev/sbi) in a python 3.9 environment.
This directory contains:
This directory contains:
- The parameters (Simulations_modelparameters.csv) and resulting MEA features (Simulations_MEAfeatures.csv) of the 300,000 simulations used to train the NDE for the paper. Every row represents one simulation. In "Simulations_modelparameters", the columns in ascending order represent the parameters: 'noise', '$g_{Na}$', '$g_{K}$', '$g_{AHP}$', '$g_{AMPA}$', '$g_{NMDA}$', 'Conn%', r'$\tau_{D}$', 'U (STD)', 'U asyn'. In "Simulations_MEAfeatures", the columns in ascending order represent the MEA features: 'MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'mean ISI CC', 'sd ISI CC', 'ISI dist', 'mean ISI', 'sd ISI temp', 'sd isi elec', 'MAC'
- The parameters (Simulations_modelparameters.csv) and resulting MEA features (Simulations_MEAfeatures.csv) of the 300,000 simulations used to train the NDE for the paper. Every row represents one simulation. In "Simulations_modelparameters", the columns in ascending order represent the parameters: 'noise', '$g_{Na}$', '$g_{K}$', '$g_{AHP}$', '$g_{AMPA}$', '$g_{NMDA}$', 'Conn%', r'$\tau_{D}$', 'U (STD)', 'U asyn'. In "Simulations_MEAfeatures", the columns in ascending order represent the MEA features: 'MFR', 'NBR', 'NBD', 'PSIB', '#FBs', 'CVIBI', 'mean CC', 'sd CC', 'mean ISI CC', 'sd ISI CC', 'ISI dist', 'mean ISI', 'sd ISI temp', 'sd isi elec', 'MAC'
- The "TrainedNDE": the neural density estimator trained using the 300,000 simulations in the mackelab sbi package. This can be loaded in python to evaluate and obtain the posterior distribution as described in 'FindPosterior.py".
- The "TrainedNDE": the neural density estimator trained using the 300,000 simulations in the mackelab sbi package. This can be loaded in python to evaluate and obtain the posterior distribution as described in 'FindPosterior.py".
