Article

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 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.

Creator

• Victor, Jonathan D
• Ermentrout, G Bard
• Connor, Erin G
• Crimaldi, John P
• McHugh, Margaret
• Nagel, Katherine I
• Boie, Sebastian D

Date Issued

• 2018-07-01

Academic Affiliation

• Civil, Environmental and Architectural Engineering

Journal Title

• PLoS Computational Biology

Journal Issue/Number

• 7

Journal Volume

• 14

File Extent

• 1006275-1006275

Subject

• Spatial Navigation
• Behavior
• Cues
• Odorants
• Information Theory
• Fluorescence
• Olfactory Receptor Neurons
• Animal
• Animals
• Smell
• Algorithms

Last Modified

• 2019-12-06

Identifier

• PubMed ID: 29990365

Resource Type

• Article

Rights Statement

• In Copyright

DOI

•  https://doi.org/10.1371/journal.pcbi.1006275

ISSN

• 1553-7358

Language

• English [eng]

License

• Creative Commons BY Attribution 4.0 International

Relationships

Items

Thumbnail	Title	Date Uploaded	Visibility	Actions
	informationTheoreticAnalysisOfRealisticOdorPlumesWhatCue.pdf	2019-12-06	Public	Download