AI 中文总结
该研究提出LLantia方法，结合连接组与文献，分层推断神经环路及细胞类型功能，以果蝇大脑为对象验证方法，为神经环路研究提供支撑。
AI 中文摘要
连接组图谱绘制的成功，将理解神经系统的挑战转向对神经环路的解读。本文设计了一种新的自动化方法LLantia（LLM automated neural circuit inference and analysis，即大型语言模型自动化神经环路推断与分析），用于系统推断神经环路功能及其组成神经细胞类型的作用。该方法从文献中提炼细胞类型功能的描述，并结合连接组，进而推断所有其他细胞类型的功能，以此作为后续神经环路功能推断的基础。结果采用分层结构，不同可能的环路功能被组织在多种可能的行为和生理背景下，每个环路功能由子环路描述及相关细胞类型组成，便于回溯至已知的已发表信息并支持进一步的实验研究。本文通过推断成年果蝇大脑所有细胞类型的功能及其中选定的更广泛环路来演示该方法，并通过与分析发布日期之后发表的文献交叉核对等方式验证了研究结果。
英文摘要
The success of connectome mapping now shifts the challenge of understanding the nervous system to the interpretation of neural circuits. Here, we devise a new automated method, LLantia (LLM automated neural circuit inference and analysis), to systematically infer neural circuit function and the role of its component neural cell types. Our approach distills descriptions of cell type function from the literature and, in combination with the connectome, then infers the function for all other cell types, which serves as a basis for subsequent neural circuit function inference. Results are structured hierarchically, with different possible circuit functions organised under multiple possible behavioural and physiological contexts, and each circuit function composed of subcircuit descriptions alongside relevant cell types to facilitate both backtracking to known, published information and support further experimental research. We illustrate our method by inferring cell type function for all cell types of the adult fruit fly brain and for select broader circuits within, and validate our findings, including by cross-checking with literature published after the release date of our analysis.
Comments26 pages, 7 figures
