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

• URL: http://arxiv.org/abs/2605.15733v1
• Date: Fri, 15 May 2026 08:36:40 GMT
• Title: Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
• Abstract summary: We show how a brain-inspired hierarchical model infers latent transitions and constructs a predictive visual world model.<n>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.
• Score: 11.849694189409526
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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