Implement and Validation of a Probabilistic Generative Model Inspired by Hippocampal Formation

Abstract

We construct and implement a concrete computational model based on a hippocampal formation-inspired probabilistic generative model (HF-PGM) and evaluate the effectiveness of the proposed model. HF-PGM does not specify the architecture or probability distribution of the model. In this study, we propose a probabilistic generative model consistent with HF-PGM by integrating the Recurrent State-Space Model (RSSM), one of the world models, and Simultaneous Localization and Mapping (SLAM)'s model based on the occupancy grid map. Global localization was performed in a simulated environment to evaluate its performance in experiments. We showed that the proposed model improves performance over conventional self-localization methods. We also evaluated the performance of the integrated world model concerning location categorization using a latent space representation.