‘Infotaxis’ as a strategy for searching without gradients

Information trail Chemotactic bacteria are guided towards the source of a nutrient by local concentration gradients. That works on the microscopic scale, but at larger scales such local cues are unreliable pointers — for example, wind or water currents may disperse odours sought by foraging animals....

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Published inNature Vol. 445; no. 7126; pp. 406 - 409
Main Authors Vergassola, Massimo, Villermaux, Emmanuel, Shraiman, Boris I.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 25.01.2007
Nature Publishing
Nature Publishing Group
Subjects
Online AccessGet full text
ISSN0028-0836
1476-4687
1476-4687
1476-4679
DOI10.1038/nature05464

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Abstract Information trail Chemotactic bacteria are guided towards the source of a nutrient by local concentration gradients. That works on the microscopic scale, but at larger scales such local cues are unreliable pointers — for example, wind or water currents may disperse odours sought by foraging animals. Using statistical techniques, Vergassola et al . have developed a general search algorithm for movement strategies based on the detection of sporadic cues and partial information. The strategy, termed 'infotaxis' as it maximizes the expected rate of information gain, could find application in the design of 'sniffer' robots. A computational model of odour plume propagation and experimental data are used to devise a general search algorithm for movement strategies in chemotaxis, based on sporadic cues and partial information. The strategy is termed 'infotaxis' as it locally maximizes the expected rate of information gain. Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient 1 . Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents 2 , 3 , 4 , 5 , 6 . A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call ‘infotaxis’, designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows 7 . Infotactic trajectories feature ‘zigzagging’ and ‘casting’ paths similar to those observed in the flight of moths 8 . The proposed search algorithm is relevant to the design of olfactory robots 9 , 10 , 11 , but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.
AbstractList Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths. The proposed search algorithm is relevant to the design of olfactory robots, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.
Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths. The proposed search algorithm is relevant to the design of olfactory robots, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information. [PUBLICATION ABSTRACT]
Information trail Chemotactic bacteria are guided towards the source of a nutrient by local concentration gradients. That works on the microscopic scale, but at larger scales such local cues are unreliable pointers — for example, wind or water currents may disperse odours sought by foraging animals. Using statistical techniques, Vergassola et al . have developed a general search algorithm for movement strategies based on the detection of sporadic cues and partial information. The strategy, termed 'infotaxis' as it maximizes the expected rate of information gain, could find application in the design of 'sniffer' robots. A computational model of odour plume propagation and experimental data are used to devise a general search algorithm for movement strategies in chemotaxis, based on sporadic cues and partial information. The strategy is termed 'infotaxis' as it locally maximizes the expected rate of information gain. Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient 1 . Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents 2 , 3 , 4 , 5 , 6 . A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call ‘infotaxis’, designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows 7 . Infotactic trajectories feature ‘zigzagging’ and ‘casting’ paths similar to those observed in the flight of moths 8 . The proposed search algorithm is relevant to the design of olfactory robots 9 , 10 , 11 , but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.
Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths. The proposed search algorithm is relevant to the design of olfactory robots, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths. The proposed search algorithm is relevant to the design of olfactory robots, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.
Audience Academic
Author Shraiman, Boris I.
Villermaux, Emmanuel
Vergassola, Massimo
Author_xml – sequence: 1
  givenname: Massimo
  surname: Vergassola
  fullname: Vergassola, Massimo
  organization: CNRS URA 2171, Institut Pasteur, “In Silico Genetics”
– sequence: 2
  givenname: Emmanuel
  surname: Villermaux
  fullname: Villermaux, Emmanuel
  organization: Université Aix Marseille 1, IRPHE, Technopole Chateau Gombert
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  givenname: Boris I.
  surname: Shraiman
  fullname: Shraiman, Boris I.
  email: shraiman@kitp.ucsb.edu
  organization: Kavli Institute for Theoretical Physics, University of California
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https://www.ncbi.nlm.nih.gov/pubmed/17251974$$D View this record in MEDLINE/PubMed
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Keywords Simulation
Taxis
Bacteria
Trajectory
Detection
Mathematical model
Infotaxis
Algorithm
Odor
Language English
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Snippet Information trail Chemotactic bacteria are guided towards the source of a nutrient by local concentration gradients. That works on the microscopic scale, but...
Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient. Such local cues pointing towards the location of the...
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SubjectTerms Algorithms
Animals
Bacteria
Bacteriology
Biochemistry
Biological and medical sciences
Biomimetics - methods
Chemotaxis - physiology
Computer Simulation
Engineering Sciences
Entropy
Experimental data
Fluid mechanics
Fluids mechanics
Fundamental and applied biological sciences. Psychology
General aspects
Humanities and Social Sciences
letter
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Mechanics
Microbiology
Models, Biological
Moths - physiology
Motility, taxis
multidisciplinary
Nutrient concentrations
Odorants - analysis
Odors
Pheromones - analysis
Physics
Robotics - methods
Robots
Science
Science (multidisciplinary)
Search engines
Smell
Smell - physiology
Wind
Title ‘Infotaxis’ as a strategy for searching without gradients
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