受海马-内嗅皮层启发，构建可抽象结构的自监督世界模型。
Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model
- 用逆模型提取结构，分离关系与场景
- 通过速度驱动路径积分实现跨场景结构复用
- 适合研究脑启发学习与抽象知识迁移者
人类将经验抽象为结构化表征以促进模式推断和知识迁移。尽管海马-内嗅皮层（HPC-MEC）回路被证实能表征空间与概念空间，但如何从连续高维动态中同时提取抽象结构仍不明确。本文提出一种受大脑启发的分层模型，同步推断潜在转移并构建预测性视觉世界模型。架构采用逆模型进行结构提取，并结合HPC-MEC耦合机制，分离内嗅皮层（MEC）的关系结构与海马（HPC）的整合情景。以原始变换动力学为基准，验证了模型的结构抽象能力。通过速度驱动的路径积分，该框架在不同情境下实现鲁棒预测与结构复用，达成结构泛化。本工作为脑启发的自监督世界模型如何获取可复用的抽象知识提供了新计算框架。
原文摘要 · Abstract (English)
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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