Title: Learning and Reusing Abstract Latent Actions in a Hippocampal-Entorhinal-Inspired World Model
Primary area: applications to neuroscience & cognitive science
Keywords: brain-inspired model, hippocampal-entorhinal coupling, inverse model, latent action, structural generalization, self-supervised learning

Abstract: Humans are capable of abstracting dynamic experiences into structured representations, facilitating both the inference of shared patterns by observing similar transition dynamics and the transfer of these structures across varied contexts. The hippocampal-entorhinal circuit, widely known for its role in spatial navigation, also supports the representation of abstract conceptual spaces crucial for non-spatial cognitive processes. This function emerges from the distinct yet integrated encoding of content-specific details by the hippocampus and abstract structures by the entorhinal cortex, facilitating structural generalization across varied contexts. Although the hippocampal-entorhinal circuit has been previously explored as a predictive system for binding contents, the process for concurrently extracting abstract structures from continuous real-world dynamics remains largely understudied. In this work, we propose a computational model inspired by the hippocampal-entorhinal circuit, capable of simultaneously inferring latent actions to form abstract structures and constructing predictive world models from real-world video sequences. Our model combines an inverse model for extracting abstract latent actions with a hippocampal-entorhinal-inspired coupling model that separately encodes contents and structures, leveraging action-driven path integration for prediction. Experimental results demonstrate that our model effectively captures abstract latent actions, reuses them robustly across diverse contexts, and achieves reliable predictive performance in both familiar and novel environments. Additionally, our analysis of latent representations from 3D object rotation datasets highlights why latent actions extracted through entorhinal cortex representations demonstrate greater abstraction and reusability. This work provides novel insights into the brain-inspired mechanisms underlying the self-supervised learning of abstract latent actions and world models from real-world dynamics, illuminating cognitive processes essential for transfer learning and data-efficient learning.

TL;DR: We propose a hippocampal-entorhinal-inspired model that effectively captures abstract latent actions, reuses them robustly across diverse contexts, and achieves reliable predictive performance in both familiar and novel environments.