AI 中文总结
该研究围绕果蝇早期嗅觉系统的连接组，指出其前馈通路嵌入密集局部反馈回路，强调需结合自然气味剂模型与反馈回路抽象，探索其功能逻辑。
AI 中文摘要
近几十年来，果蝇的早期嗅觉系统（EOS）已成为研究嗅觉加工和联想记忆的领先模型，部分原因在于其特征明确的前馈通路，该通路为联想记忆的基础过程提供输入，且可通过研究少数神经元和突触的作用来开展分析。近年来，密集电子显微镜连接组的完成，提供了早期嗅觉通路中每种细胞类型、神经元和突触的高质量可视化结果。然而，无论多么完整的连接图谱，本身都无法揭示神经回路的功能逻辑。通过回顾过去十五年的EOS连接组和突触组数据集，我们发现前馈通路嵌入在由大型多输入多输出神经元构成的密集局部反馈回路中。对反馈回路抽象结构及其调控每个神经毡阶段输入/输出变换的能力的系统理解，是早期嗅觉回路功能逻辑的基础。此外，我们认为功能逻辑的定量描述需要一个关于自然环境中存在的气味剂的明确模型，该模型由气味剂对象组成，定义了嗅觉信息加工的语义和句法，并需要新的距离度量来分类气味剂语义，以支持联想记忆操作。再者，气味信息加工必须遵循因果关系，将回路视为由巨型局部反馈回路构成的实时、逐阶段级联结构。
英文摘要
In recent decades, the early olfactory system (EOS) of the fruit fly has become a leading model for studying olfactory processing and associative memory, owing in part to a well-characterized feedforward pathway that feeds the processes underlying associative memory and by examining the role played by a handful of neurons and synapses. The recent completion of dense electron-microscopy connectomes provides high quality visualizations of every cell type, neuron, and synapse along the early olfactory pathway. Yet a wiring diagram, however complete, does not by itself reveal the functional logic of a neural circuit. Reviewing the EOS connectome and synaptome datasets of the past fifteen years, we note that the feedforward pathway is embedded in dense local feedback circuits of large scale multi-input multi-output neurons. A systematic understanding of feedback loop abstractions, and their capacity to govern the input/output transformations at each neuropil stage, is the underlying foundation of the functional logic of the early olfactory circuits. In addition, we argue that a quantitative account of the functional logic requires an explicit model of the odorants present in the natural environment. Consisting of odorant objects, such a model defines the semantics and syntax of olfactory information processing, and calls for new distance measures for classifying the odorant semantics in support of associative memory operations. Furthermore, odor information processing must abide by causality, treating the circuit as a real-time, stage-by-stage cascade of giant local feedback loops.
