Data and scripts used in: "Exploring Biological Neuronal Correlations with Quantum Generative Models"

Data and script for the manuscript "Exploring Biological Neuronal Correlations with Quantum Generative Models", by Vinicius Hernandes and Eliska Greplova.

main scripts

generate_activity_dataset.py

reshape data in neuronData.npy to 50k samples of (neurons, timesteps) shape, saved in activity_data.npy

create_target_distributions.py

based on the dataset, makes dicionary with the the target distribution for each (neurons, timesteps) pair, saved in distribution_target_dictionary.pkl

create_hyperparameters_file.py

generates hyperparameters.csv, containing:

number of neurons

number of timesteps

number of auxiliary_qubits

batch_size

learning rate of generator

learning rate of critic

number parametrized layers

number of training iterations

loss type

for each run

train_qgan.py

trains models defined models.py using activity_data.npy dataset, and for the hyperparameters defined in hyperparameters.csv

saves loss functions, and the trained models for each 10 iterations, in specific folders indexed by the run specified in the hyperparameters file

generate_fake_activity.py

uses trained models saved in output/models/run{run}/i{training_step}.pth for a specific training_step and run to generate fake data, and save them in output/generated_data/run{run}/i{training_step}.npy files

analyze_error.py

uses generated data saved in output/generated_data/run{run}/i{training_step}.npy to generate two statistical quantities (k-probs and firing rate), using the function in metrics.py, and compare the errors in those quantities between the models using k-loss and standard-loss

analyze_stats.py

uses generated data saved in output/generated_data/run{run}/i{training_step}.npy to generate:

js diverge for each training step, and final distribution of generated states, stored in distribution_target_dictionary.pkl

other statistical quantities, using the function in metrics.py file

auxiliary scripts

metrics.py

functions to calculate neuronal statistics

aux.py

auxiliary functions:

to generate states distribution given a dataset

custom js divergence

Data

neuronData.npy

neuronal data from Marre et al., Multi-electrode array recording from salamander retinal ganglion cells (2017)

activity_data.npy

dataset obtained from neuronData.npy, taking 50 thousand samples of shape (neurons, timesteps)

output

results obtained from train_qgan.py and generate_fake_activity.py 

contains:

losses

generator and critic loss for all training runs and steps

models

saved torch models every 10 training steps, for all training runs

generated_data

generated data for all models saved in models