  We present a novel artificial cognitive mapping system using generative deep
neural networks (VAE/GAN), which can map input images to latent vectors and
generate temporal sequences internally. The results show that the distance of
the predicted image is reflected in the distance of the corresponding latent
vector after training. This indicates that the latent space is constructed to
reflect the proximity structure of the data set, and may provide a mechanism by
which many aspects of cognition are spatially represented. The present study
allows the network to internally generate temporal sequences analogous to
hippocampal replay/pre-play, where VAE produces only near-accurate replays of
past experiences, but by introducing GANs, latent vectors of temporally close
images are closely aligned and sequence acquired some instability. This may be
the origin of the generation of the new sequences found in the hippocampus.
