  The ability to synthesize realistic patterns of neural activity is crucial
for studying neural information processing. Here we used the Generative
Adversarial Networks (GANs) framework to simulate the concerted activity of a
population of neurons. We adapted the Wasserstein-GAN variant to facilitate the
generation of unconstrained neural population activity patterns while still
benefiting from parameter sharing in the temporal domain. We demonstrate that
our proposed GAN, which we termed Spike-GAN, generates spike trains that match
accurately the first- and second-order statistics of datasets of tens of
neurons and also approximates well their higher-order statistics. We applied
Spike-GAN to a real dataset recorded from salamander retina and showed that it
performs as well as state-of-the-art approaches based on the maximum entropy
and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not
require to specify a priori the statistics to be matched by the model, and so
constitutes a more flexible method than these alternative approaches. Finally,
we show how to exploit a trained Spike-GAN to construct 'importance maps' to
detect the most relevant statistical structures present in a spike train.
Spike-GAN provides a powerful, easy-to-use technique for generating realistic
spiking neural activity and for describing the most relevant features of the
large-scale neural population recordings studied in modern systems
neuroscience.
