用局部学习规则实现记忆在线构建认知地图。
A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
- 基于海布式学习规则，实现无全局优化的在线记忆结构化。
- 在部分可观测网格世界中自主构建结构化认知地图。
- 适合关注生物可解释性与智能体自适应的学习者。
认知地图为理解生物与人工智能体的空间及抽象推理提供了有力框架。尽管近期计算模型将认知地图与海马-内嗅皮层机制关联，但通常依赖缺乏生物合理性的全局优化规则（如反向传播）。本文提出一种新型认知架构，利用局部海布式学习规则，将情景记忆逐步结构化为认知地图，符合神经基质约束。该模型融合成功特征框架与情景记忆，通过智能体-环境交互实现增量式在线学习。我们在部分可观测网格世界中验证其有效性，证明该架构可在无需集中优化的情况下自主组织记忆为结构化表征。本工作连接计算神经科学与人工智能，为人工自适应智能体的认知地图形成提供生物基础路径。
原文摘要 · Abstract (English)
Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neural substrate constraints. Our model integrates the Successor Features framework with episodic memories, enabling incremental, online learning through agent-environment interaction. We demonstrate its efficacy in a partially observable grid-world, where the architecture autonomously organizes memories into structured representations without centralized optimization. This work bridges computational neuroscience and AI, offering a biologically grounded approach to cognitive map formation in artificial adaptive agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。