发表机构
The University of Texas MD Anderson Cancer Center; The University of Tennessee Health Science Center（德克萨斯大学MD安德森癌症中心; 田纳西大学健康科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比大脑与LLMs的记忆系统，提出可利用LLMs在实验访问上的优势，将记忆相关功能问题转化为更精准的生物学假设，核心是转移实验逻辑而非解剖结构。
AI 中文摘要
大脑与大型语言模型（LLMs）是本质不同的记忆系统，但可通过共同的功能问题进行比较：记忆相关信息的表征位置、部分线索如何恢复更广泛的关联、新信息如何写入或更新、记忆相关状态如何被扰动。在生物系统中，这些问题涉及突触、神经元集群、海马-皮层相互作用及可塑性；在LLMs中，涉及权重、激活、上下文窗口、检索系统及外部存储。因此，这种比较是功能和实验层面的，而非解剖层面的。人类研究揭示了稀疏概念响应、时间绑定、快速关联形成、情景特异性编码及回忆相关的再激活，但选择性干预仍有限。啮齿动物研究能更选择性地因果访问学习相关集群，而人类和猕猴的干预通常会影响更广泛的回路。LLMs缺乏活体情景记忆，但允许对内部状态和存储信息进行异常直接且可重复的操作。我们认为这种不对称性创造了新机会：LLMs并非在记忆本身更先进，而是在实验访问上更具优势，其工具可助力将关于检索、更新、持久性、可逆性及非预期效应的广泛问题转化为更精准的生物学假设，有效桥梁是转移实验逻辑，而非解剖结构。
英文摘要
Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information is represented, how partial cues recover broader associations, how new information is written or updated, and how memory-related states can be perturbed. In biological systems, these questions span synapses, neuronal ensembles, hippocampal-cortical interactions, and plasticity; in LLMs, they span weights, activations, context windows, retrieval systems, and external stores. The comparison is therefore functional and experimental rather than anatomical. Human studies reveal sparse concept responses, temporal binding, rapid association formation, episode-specific coding, and recall-related reactivation, but selective intervention remains limited. Rodent studies provide more selective causal access to learning-related ensembles, whereas human and macaque interventions usually affect broader circuits. LLMs lack lived episodic memory, yet they permit unusually direct and repeatable manipulation of internal states and stored information. We argue that this asymmetry creates a new opportunity. LLMs are not ahead in memory itself, but in experimental access. Their tools may help turn broad questions about retrieval, updating, persistence, reversibility, and unintended effects into sharper biological hypotheses. The productive bridge is to transfer experimental logic, not anatomical parts.
CommentsPerspective article, 11 pages, 3 figures, 1 table, and 1 key terms box. Submitted for consideration to Nature Machine Intelligence
