arXiv:2608.00059cs.CLcs.AI2026-08

用大模型自动推断果蝇脑神经环路功能,实现系统化解析。

Neural Circuit Function Inference with LLMs

论文配图:Neural Circuit Function Inference with LLMs
图 1 · 摘自论文原文
  • 基于文献与连接组数据,用大模型推断神经元类型功能
  • 构建分层结构,涵盖多种行为与生理情境下的环路功能
  • 可追溯已有研究,适合神经科学与计算建模方向读者

连接组绘制的成功将神经系统理解的挑战转向神经环路功能的解析。我们提出一种新方法LLantia(LLM自动化神经环路功能推断与分析),系统推断神经环路功能及其组成神经元类型的职责。该方法从文献中提炼神经元类型功能描述,结合连接组数据,推断所有其他神经元类型的功能,作为后续环路功能分析的基础。结果以分层结构组织,涵盖多种可能的行为与生理情境,每个环路功能由子环路描述和相关神经元类型构成,便于回溯已知文献并支持进一步实验研究。我们以成年果蝇脑所有神经元类型及部分扩展环路为例,展示了该方法的应用,并通过发布后发表的文献交叉验证了结果。

原文摘要 · Abstract (English)

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.

神经环路大模型果蝇脑

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