Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model

2026-05-15

AI Summary

This study addresses the challenge of simultaneously extracting abstract relational structures from continuous high-dimensional dynamic experiences and enabling cross-context knowledge transfer. Inspired by the neural mechanisms of the hippocampus (HPC) and medial entorhinal cortex (MEC), the work proposes the first self-supervised hierarchical world model that functionally disentangles these two brain regions: an inverse model captures relational structure, while velocity-driven path integration decouples structural representations in the MEC from episodic details encoded in the HPC. This framework unifies structure discovery and reuse, demonstrating superior abstraction capabilities on raw transformation-based dynamic tasks and achieving robust prediction and generalization across multiple contexts.

Abstract

Humans abstract experiences into structured representations to facilitate pattern inference and knowledge transfer. While the hippocampal-entorhinal (HPC-MEC) circuit is known to represent both spatial and conceptual spaces, the mechanisms for concurrently extracting abstract structures from continuous, high-dimensional dynamics remain poorly understood. We propose a brain-inspired hierarchical model that simultaneously infers latent transitions and constructs a predictive visual world model. Our architecture employs an inverse model for structural extraction alongside an HPC-MEC coupling model that dissociates relational structures (MEC) from integrated episodic scenes (HPC). Using primitive transformation dynamics as a benchmark, we demonstrate the model's capacity for structural abstraction. By leveraging velocity-driven path integration, the framework enables robust prediction and structural reuse across diverse contexts, thereby achieving structural generalization. This work provides a novel computational framework for understanding how brain-inspired, self-supervised learning of world models facilitates the acquisition of reusable abstract knowledge.

Problem

• structural abstraction
• structural generalization
• hippocampal-entorhinal circuit
• world model
• high-dimensional dynamics

Innovation

• structural abstraction
• hippocampal-entorhinal circuit
• world model
• path integration
• self-supervised learning