- 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".
- The "Simulator.py" contains the functions to perform simulations with the biophysical computational model of hiPSC-derived neuronal networks on MEA. The "elecranges.dat" is necessary for this.
- "Simulator.py" contains the functions to perform simulations with the biophysical computational model of hiPSC-derived neuronal networks on MEA. The "elecranges.dat" is necessary for this.
- "FeatureExtraction.py" has the function to extract the MEA features from spike trains of either either simulations or experimental data. It also has a function to compute spike rates per electrode that can be used as input to an embedding network.
- "FeatureExtraction.py" has the function to extract the MEA features from spike trains of either either simulations or experimental data. It also has a function to compute spike rates per electrode that can be used as input to an embedding network.
