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.

翻译：人类将经验抽象为结构化表征，以促进模式推理和知识迁移。尽管已知海马-内嗅皮层回路既能表征空间空间也能表征概念空间，但关于其如何从连续高维动态中并发提取抽象结构的机制仍知之甚少。我们提出了一种受大脑启发的层次化模型，该模型可同时推断潜在状态转换并构建预测性视觉世界模型。架构采用逆向模型进行结构提取，并结合了海马-内嗅皮层耦合模型，该模型将关系结构（内嗅皮层）与整合情景场景（海马体）解耦。以原始变换动力学为基准，我们验证了模型的结构抽象能力。通过利用速度驱动的路径积分，该框架实现了跨不同上下文的鲁棒预测和结构复用，从而达成结构泛化。本研究为理解受大脑启发的世界模型自监督学习如何促进可复用抽象知识的获取提供了新颖的计算框架。