Animals use odors in many natural contexts, for example, for finding mates or food, or signaling danger. Most analyses of natural odors search for either the most meaningful components of a natural odor mixture, or they use linear metrics to analyze the mixture compositions. However, we have recently shown that the physical space for complex mixtures is ‘hyperbolic’, meaning that there are certain combination of variables that have a disproportionately large impact on perception and that these variables have specific interpretation in terms of metabolic processes taking place inside the flower and fruit that produce the odors. Here we show that the statistics of odorants and odorant mixtures produced by inflorescences (Brassica rapa) are also better described with a hyperbolic rather than a linear metric, and that combinations of odorants in the hyperbolic space are better predictors of the nectar and pollen resources sought by bee pollinators than the standard Euclidian combinations. We also show that honey bee and bumble bee antennae can detect most components of the B. rapa odor space, and the strength of responses correlates with positions of odorants in the hyperbolic space. In sum, a hyperbolic representation can be used to guide investigation of how information is represented at different levels of processing in the CNS.

中文翻译：

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双曲线气味混合物作为开花植物和蜜蜂之间更有效信号传递的基础

动物在许多自然环境中使用气味，例如寻找配偶或食物，或发出危险信号。大多数自然气味分析要么搜索自然气味混合物中最有意义的成分，要么使用线性指标来分析混合物成分。然而，我们最近表明复杂混合物的物理空间是“双曲线的”，这意味着某些变量组合对感知产生了不成比例的巨大影响，并且这些变量在内部发生的代谢过程方面具有特定的解释。产生气味的花和果实。在这里，我们展示了由花序 ( Brassica rapa)产生的气味和气味混合物的统计数据) 也可以用双曲线而不是线性度量更好地描述，并且双曲线空间中的气味组合比标准欧几里得组合更能预测蜜蜂传粉者寻求的花蜜和花粉资源。我们还表明，蜜蜂和大黄蜂的触角可以检测到B. rapa气味空间的大部分成分，并且响应强度与双曲线空间中气味剂的位置相关。总之，双曲线表示可用于指导研究信息在 CNS 的不同处理级别如何表示。