Spike-GAN

Tensorflow implementation of Spike-GAN, which allows generating realistic patterns of neural activity whose statistics approximate a given traing dataset (Molano-Mazon et al. 2018 ICRL2018).

Prerequisites

• Python 3.5+
• Tensorflow 1.7.
• Numpy.
• SciPy.
• Matplotlib.

All necessary modules can be installed via Anaconda.

Installing

Just download the repository to your computer and add the Spike-GAN folder to your path.

Data

The folder named data contains the retinal data used for Fig. 3 in the ICLR paper. The whole data set can be found in Marre et al. 2014. The folder also contains the data generated by the k-pairwise and the Dichotomized-Gaussian models for that same figure and for Fig. S6. Spike-GAN can be run using this data with the command:

python3.5 main_conv.py --architecture='conv' --dataset='retina' --num_bins=32 --iteration='test' --num_neurons=50 --is_train --num_layers=2 --num_features=128 --kernel_width=5 --data_instance='1'

Example

The example below will train Spike-GAN with the semi-convolutional architecture on a simulated dataset containing the activity of two correlated neurons whose firing rate follows a uniform distribution across 12 ms. See main_conv.py for more options on the type of simulated activity (refractory period, firing rate...).

python3.5 main_conv.py --is_train --architecture='conv' --dataset='uniform' --num_bins=12 --num_neurons=2 

Authors

• Manuel Molano.
• Arno Onken.
• Eugenio Piasini.

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 699829 (ETIC).