Information-theoretic analysis of realistic odor plumes: What cues are useful for determining location?
Information-theoretic analysis of realistic odor plumes: What cues are useful for determining location?
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DOI：
10.1371/journal.pcbi.1006275
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发表时间：
2018-07
影响因子：
4.3
通讯作者：
Victor JD
中科院分区：
文献类型：
--
作者：
Boie SD;Connor EG;McHugh M;Nagel KI;Ermentrout GB;Crimaldi JP;Victor JD
许多物种依靠嗅觉来寻找食物来源或配偶。嗅觉导航是一项具有挑战性的任务，因为气味环境通常是动荡的。虽然时间平均气味浓度随着距离源的距离而平稳变化，但瞬时浓度是间歇性的，获得稳定的平均值比动物导航决策之间的典型间隔时间要长。如何有效地从气味分布中进行采样，确定采样位置是本文研究的重点。为了研究哪种采样策略最能提供气味源位置的信息，我们用平面激光诱导荧光记录了三种自然刺激，并使用信息论方法量化了不同采样策略提供的采样位置信息。具体来说，我们比较了基于固定编码比特数的多种采样策略来编码嗅觉刺激。当编码位都被分配到表示单个传感器的气味浓度时，信息迅速饱和。在两个传感器中使用相同数量的编码位可以提供更多的信息，在不同时间对多个样本进行编码也是如此。当在固定位置积累多个样本时，时间序列不会产生大量信息，可以以最小的损失进行平均。此外，我们表明，当使用嗅觉样本确定位置时，直方图均衡化并不是使用编码位的最有效方法。由于气味分子的时空分布复杂，基于动物的嗅觉导航到食物来源或交配对象是一项艰巨的任务。这项任务最基本的方面是从环境中获取样本。很明显，在许多自然觅食环境中，气味浓度在空间上的变化并不平稳。使用来自三种不同自然环境的数据，我们比较了不同的采样策略，并评估了它们在确定源位置方面的有效性。我们的研究结果表明，在单独的传感器和/或多次对样品的浓度进行粗编码比在更高分辨率下编码更少的样品提供更多的信息。此外，编码资源应该集中在识别罕见的高浓度气味样本上，这些样本对采样位置有很大的信息。这种非线性转换可以通过结合气味剂作为采样过程的第一阶段的受体结合动力学来实现。进一步的暗示是，动物以及算法的计算模型可以有效地使用气味浓度的粗略表示。
Many species rely on olfaction to navigate towards food sources or mates. Olfactory navigation is a challenging task since odor environments are typically turbulent. While time-averaged odor concentration varies smoothly with the distance to the source, instaneous concentrations are intermittent and obtaining stable averages takes longer than the typical intervals between animals’ navigation decisions. How to effectively sample from the odor distribution to determine sampling location is the focus in this article. To investigate which sampling strategies are most informative about the location of an odor source, we recorded three naturalistic stimuli with planar lased-induced fluorescence and used an information-theoretic approach to quantify the information that different sampling strategies provide about sampling location. Specifically, we compared multiple sampling strategies based on a fixed number of coding bits for encoding the olfactory stimulus. When the coding bits were all allocated to representing odor concentration at a single sensor, information rapidly saturated. Using the same number of coding bits in two sensors provides more information, as does coding multiple samples at different times. When accumulating multiple samples at a fixed location, the temporal sequence does not yield a large amount of information and can be averaged with minimal loss. Furthermore, we show that histogram-equalization is not the most efficient way to use coding bits when using the olfactory sample to determine location. Navigating towards a food source or mating partner based on an animals’ sense of smell is a difficult task due to the complex spatiotemporal distribution of odor molecules. The most basic aspect of this task is the acquisition of samples from the environment. It is clear that odor concentration does not vary smoothly across space in many natural foraging environments. Using data from three different naturalistic environments, we compare different sampling strategies and assess their efficacy in determining the sources’ location. Our findings show that coarsely encoding the concentration of samples at separate sensors and/or multiple times provides more information than encoding fewer samples with higher resolution. Furthermore, coding resources should be focused on discriminating rare high-concentration odor samples, which are very informative about the sampling location. Such a nonlinear transformation can be implemented biologically by the receptor binding kinetics that bind odorants as a first stage of the sampling process. A further implication is that animals as well as computational models of algorithms can operate efficiently with a coarse representation of the odor concentration.
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影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
嗅觉感觉神经元中果蝇化学感受器的独特信号传导
DOI：
10.1073/pnas.1518329113
发表时间：
2016-02-16
影响因子：
11.1
作者：
Cao, Li-Hui;Jing, Bi-Yang;Luo, Dong-Gen
通讯作者：
Luo, Dong-Gen
影响因子：
7.7
作者：
Gorur-Shandilya, Srinivas;Demir, Mahmut;Emonet, Thierry
通讯作者：
Emonet, Thierry
影响因子：
25
作者：
Nagel, Katherine I.;Wilson, Rachel I.
通讯作者：
Wilson, Rachel I.
影响因子：
64.8
作者：
MAFRANETO, A;CARDE, RT
通讯作者：
CARDE, RT