标题： 迈向视觉-语言情景记忆框架：大规模预训练模型增强的海马吸引子动力学 显示英文标题

标题： Towards a Vision-Language Episodic Memory Framework: Large-scale Pretrained Model-Augmented Hippocampal Attractor Dynamics

摘要： 建模情景记忆（EM）在神经科学和人工智能领域仍然是一个重大挑战，现有的模型要么缺乏可解释性，要么难以应用于实际场景。 本文提出了视觉-语言情景记忆（VLEM）框架，通过整合大规模预训练模型与海马体吸引子动力学来解决这些挑战。 VLEM 利用预训练模型强大的语义理解能力，将感觉输入转化为语义嵌入，类似于新皮层的功能，而海马体则通过吸引子动力学支持稳定的记忆存储和检索。 此外，VLEM 结合了前额叶工作记忆和内嗅皮层门户，允许新皮层与海马体之间的交互。 为了促进实际应用，我们引入了EpiGibson，这是一个三维模拟平台，用于生成情景记忆数据。 实验结果表明，VLEM 框架能够高效地从感觉输入中学习高层次的时间表示，展示了其在现实场景中的鲁棒性、可解释性和适用性。 显示英文摘要

摘要： Modeling episodic memory (EM) remains a significant challenge in both neuroscience and AI, with existing models either lacking interpretability or struggling with practical applications. This paper proposes the Vision-Language Episodic Memory (VLEM) framework to address these challenges by integrating large-scale pretrained models with hippocampal attractor dynamics. VLEM leverages the strong semantic understanding of pretrained models to transform sensory input into semantic embeddings as the neocortex, while the hippocampus supports stable memory storage and retrieval through attractor dynamics. In addition, VLEM incorporates prefrontal working memory and the entorhinal gateway, allowing interaction between the neocortex and the hippocampus. To facilitate real-world applications, we introduce EpiGibson, a 3D simulation platform for generating episodic memory data. Experimental results demonstrate the VLEM framework's ability to efficiently learn high-level temporal representations from sensory input, showcasing its robustness, interpretability, and applicability in real-world scenarios.