  Deep generative models are reported to be useful in broad applications
including image generation. Repeated inference between data space and latent
space in these models can denoise cluttered images and improve the quality of
inferred results. However, previous studies only qualitatively evaluated image
outputs in data space, and the mechanism behind the inference has not been
investigated. The purpose of the current study is to numerically analyze
changes in activity patterns of neurons in the latent space of a deep
generative model called a "variational auto-encoder" (VAE). What kinds of
inference dynamics the VAE demonstrates when noise is added to the input data
are identified. The VAE embeds a dataset with clear cluster structures in the
latent space and the center of each cluster of multiple correlated data points
(memories) is referred as the concept. Our study demonstrated that transient
dynamics of inference first approaches a concept, and then moves close to a
memory. Moreover, the VAE revealed that the inference dynamics approaches a
more abstract concept to the extent that the uncertainty of input data
increases due to noise. It was demonstrated that by increasing the number of
the latent variables, the trend of the inference dynamics to approach a concept
can be enhanced, and the generalization ability of the VAE can be improved.
