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A Comparison between Mouse, In Silico, and Robot Odor Plume Navigation Reveals Advantages of Mouse Odor Tracking
- Source :
- eNeuro
- Publication Year :
- 2020
- Publisher :
- Society for Neuroscience, 2020.
-
Abstract
- Localization of odors is essential to animal survival, and thus animals are adept at odor navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment.<br />Localization of odors is essential to animal survival, and thus animals are adept at odor navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment. To do so, we compared mouse, in silico model, and Arduino-based robot odor-localization behavior in a standardized odor landscape. Mouse performance remains robust in the presence of increased complexity, showing a shift in strategy towards faster movement with increased environmental complexity. Implementing simple binaral and temporal models of tropotaxis and klinotaxis, an in silico model and Arduino robot, in the same environment as the mice, are equally successful in locating the odor source within a plume of low complexity. However, performance of these algorithms significantly drops when the chaotic nature of the plume is increased. Additionally, both algorithm-driven systems show more successful performance when using a strictly binaral model at a larger sensor separation distance and more successful performance when using a temporal and binaral model when using a smaller sensor separation distance. This suggests that with an increasingly chaotic odor environment, mice rely on complex strategies that allow for robust odor localization that cannot be resolved by minimal algorithms that display robust performance at low levels of complexity. Thus, highlighting that an animal’s ability to modulate behavior with environmental complexity is beneficial for odor localization.
- Subjects :
- odor plume
Computer science
In silico
Movement
Chaotic
Tracking (particle physics)
Low complexity
03 medical and health sciences
Mice
0302 clinical medicine
Animals
Computer Simulation
navigation
mouse
030304 developmental biology
0303 health sciences
Temporal models
business.industry
General Neuroscience
musculoskeletal, neural, and ocular physiology
turbulence
Pattern recognition
robot
General Medicine
Robotics
New Research
Plume
Smell
Odor
in silico
8.1
Odorants
Robot
Sensory and Motor Systems
Artificial intelligence
business
030217 neurology & neurosurgery
psychological phenomena and processes
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 23732822
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- eNeuro
- Accession number :
- edsair.doi.dedup.....ad899a4bbb23802f2f2e47ebd2db21ee