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

TL;DR

This paper introduces a brain-inspired hierarchical model that learns structured, abstract representations of high-dimensional experiences, enabling robust prediction and generalization across diverse contexts.

Contribution

It presents a novel computational framework combining inverse modeling and hippocampal-entorhinal coupling to extract and reuse abstract structures from continuous data.

Findings

Demonstrates capacity for structural abstraction using primitive transformation dynamics

Enables robust prediction and structural reuse across contexts

Provides insights into brain-inspired self-supervised learning of world models

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.