The mixing of distant sources

Kree, Mihkel; Duplat, Jérôme; Villermaux, Emmanuel

doi:10.1063/1.4820015

Cited by 17 publications

(16 citation statements)

References 4 publications

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“…For typical concentrations, c thr C(x) and the exponential factor in (A20) reduces to exp[−(d/x) β ] : in order to discriminate two different sources by sampling typical concentrations, their separation d must be comparable to the distance x separating the receiver from one source. Our prediction agrees with experimental observations where the cross correlation between the concentration of two scalars emitted by different sources was measured [32]. Conversely, intense events carry more information and allow to tell closer sources apart.…”

Section: Appendix A: Theorysupporting

confidence: 91%

Odor Landscapes in Turbulent Environments

2014

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International audienceThe olfactory system of male moths is exquisitely sensitive to pheromones emitted by females and transported in the environment by atmospheric turbulence. Moths respond to minute amounts of pheromones, and their behavior is sensitive to the fine-scale structure of turbulent plumes where pheromone concentration is detectible. The signal of pheromone whiffs is qualitatively known to be intermittent, yet quantitative characterization of its statistical properties is lacking. This challenging fluid dynamics problem is also relevant for entomology, neurobiology, and the technological design of olfactory stimulators aimed at reproducing physiological odor signals in well-controlled laboratory conditions. Here, we develop a Lagrangian approach to the transport of pheromones by turbulent flows and exploit it to predict the statistics of odor detection during olfactory searches. The theory yields explicit probability distributions for the intensity and the duration of pheromone detections, as well as their spacing in time. Predictions are favorably tested by using numerical simulations, laboratory experiments, and field data for the atmospheric surface layer. The resulting signal of odor detections lends itself to implementation with state-of-the-art technologies and quantifies the amount and the type of information that male moths can exploit during olfactory searches

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Section: Appendix A: Theorysupporting

confidence: 91%

Odor Landscapes in Turbulent Environments

2014

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“…However, in realistic olfactory system, the time-course of receptor activation might provide additional information about which ligands are present. For instance, odors from multiple sources might not fully mix and thus arrive in distinguishable whiffs [50].…”

Section: Resultsmentioning

confidence: 99%

Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned

Zwicker

2019

PLoS Comput Biol

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The olfactory system faces the difficult task of identifying an enormous variety of odors independent of their intensity. Primacy coding, where the odor identity is encoded by the receptor types that respond earliest, might provide a compact and informative representation that can be interpreted efficiently by the brain. In this paper, we analyze the information transmitted by a simple model of primacy coding using numerical simulations and statistical descriptions. We show that the encoded information depends strongly on the number of receptor types included in the primacy representation, but only weakly on the size of the receptor repertoire. The representation is independent of the odor intensity and the transmitted information is useful to perform typical olfactory tasks with close to experimentally measured performance. Interestingly, we find situations in which a smaller receptor repertoire is advantageous for discriminating odors. The model also suggests that overly sensitive receptor types could dominate the entire response and make the whole array useless, which allows us to predict how receptor arrays need to adapt to stay useful during environmental changes. Taken together, we show that primacy coding is more useful than simple binary and normalized coding, essentially because the sparsity of the odor representations is independent of the odor statistics, in contrast to the alternatives. Primacy coding thus provides an efficient odor representation that is independent of the odor intensity and might thus help to identify odors in the olfactory cortex.

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“…Segregating an unknown odorant from a mixture is a blind source separation problem, and solving it requires more information than just the chemical odorant identity (Hendin et al, 1994). The physics of odorant dispersion adds relational information to the chemical odorant identity, as odorants from the same source form plumes with relatively stable odorant concentration proportions (homogeneous plumes), while odorants from different sources form plumes with variable odorant concentration proportions (heterogeneous plumes; Hopfield, 1991; Kree et al, 2013; Celani et al, 2014; Riffell et al, 2014; Soltys and Crimaldi, 2015; Erskine et al, 2019). Indeed, animals can use these relational stimuli to detect whether odorants originate from the same or different sources.…”

Section: Discussionmentioning

confidence: 99%

“…Natural olfactory stimuli are typically mixtures of many different odorants from different sources, which mix together in turbulent plumes (Murlis et al, 1992; Kree et al, 2013; Celani et al, 2014; Riffell et al, 2014; Soltys and Crimaldi, 2015; Erskine et al, 2019). Previous studies suggested that animals perceive odorant mixtures synthetically, that is, they perceive a mixture as a perceptual unit rather than as a list of individual odorants (humans: Jinks and Laing, 1999; squirrel monkeys: Laska and Hudson, 1993; rats: Staubli et al, 1987; spiny lobsters: Lynn et al, 1994, honey bees: Chandra and Smith, 1998; Smith, 1998; Deisig et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the turbulent structure of plumes, odorants that originate from the same source fluctuate with stable relative concentration proportions, forming a homogeneous stream of plumes, whereas odorants from different sources will have varying relative concentration proportions, creating a heterogeneous stream of plumes (Hopfield, 1991; Kree et al, 2013; Celani et al, 2014; Riffell et al, 2014; Soltys and Crimaldi, 2015; Erskine et al, 2019). Therefore, homogeneous and heterogeneous plumes could provide the animal with information about the number of odor sources and which odorants belong to the same odor source.…”

Section: Introductionmentioning

confidence: 99%

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Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees

Sehdev

Szyszka

2019

Front. Behav. Neurosci.

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Animals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.

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The mixing of distant sources

Abstract: International audienc

Cited by 17 publications

References 4 publications

Odor Landscapes in Turbulent Environments

Odor Landscapes in Turbulent Environments

Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned

Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees

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