  Spiking neural networks (SNNs) can be run on neuromorphic devices with
ultra-high speed and ultra-low energy consumption because of their binary and
event-driven nature. Therefore, SNNs are expected to have various applications,
including as generative models being running on edge devices to create
high-quality images. In this study, we build a variational autoencoder (VAE)
with SNN to enable image generation. VAE is known for its stability among
generative models; recently, its quality advanced. In vanilla VAE, the latent
space is represented as a normal distribution, and floating-point calculations
are required in sampling. However, this is not possible in SNNs because all
features must be binary time series data. Therefore, we constructed the latent
space with an autoregressive SNN model, and randomly selected samples from its
output to sample the latent variables. This allows the latent variables to
follow the Bernoulli process and allows variational learning. Thus, we build
the Fully Spiking Variational Autoencoder where all modules are constructed
with SNN. To the best of our knowledge, we are the first to build a VAE only
with SNN layers. We experimented with several datasets, and confirmed that it
can generate images with the same or better quality compared to conventional
ANNs. The code is available at https://github.com/kamata1729/FullySpikingVAE
