Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
This work offers a computational framework for understanding how hippocampal-entorhinal circuits enable structural abstraction and generalization, addressing a key gap in modeling human-like knowledge transfer.
The paper proposes a brain-inspired hierarchical model that extracts abstract structures from continuous dynamics and constructs a predictive visual world model, demonstrating structural abstraction and generalization in primitive transformation dynamics.
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.