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Information-theoretic analysis of realistic odor plumes: What cues are useful for determining location?

TitleInformation-theoretic analysis of realistic odor plumes: What cues are useful for determining location?
Publication TypeJournal Article
Year of Publication2018
AuthorsBoie SD, Connor EG, McHugh M, Nagel KI, G Ermentrout B, Crimaldi JP, Victor JD
JournalPLoS Comput Biol
Volume14
Issue7
Paginatione1006275
Date Published2018 Jul 10
ISSN1553-7358
Abstract

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 on 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.

DOI10.1371/journal.pcbi.1006275
Alternate JournalPLoS Comput. Biol.
PubMed ID29990365