  We constructed a hippocampal formation (HPF)-inspired probabilistic
generative model (HPF-PGM) using the structure-constrained interface
decomposition method. By modeling brain regions with PGMs, this model is
positioned as a module that can be integrated as a whole-brain PGM. We discuss
the relationship between simultaneous localization and mapping (SLAM) in
robotics and the findings of HPF in neuroscience. Furthermore, we survey the
modeling for HPF and various computational models, including brain-inspired
SLAM, spatial concept formation, and deep generative models. The HPF-PGM is a
computational model that is highly consistent with the anatomical structure and
functions of the HPF, in contrast to typical conventional SLAM models. By
referencing the brain, we suggest the importance of the integration of
egocentric/allocentric information from the entorhinal cortex to the
hippocampus and the use of discrete-event queues.
