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‘Infotaxis’ as a strategy for searching without gradients

Author

Listed:
  • Massimo Vergassola

    (CNRS URA 2171, Institut Pasteur, “In Silico Genetics”)

  • Emmanuel Villermaux

    (Technopole Chateau Gombert)

  • Boris I. Shraiman

    (University of California)

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.

Suggested Citation

  • Massimo Vergassola & Emmanuel Villermaux & Boris I. Shraiman, 2007. "‘Infotaxis’ as a strategy for searching without gradients," Nature, Nature, vol. 445(7126), pages 406-409, January.
    • Handle: RePEc:nat:nature:v:445:y:2007:i:7126:d:10.1038_nature05464
    DOI: 10.1038/nature05464
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    Cited by:

    1. Grzegorz Suchanek & Jerzy Wołoszyn & Andrzej Gołaś, 2022. "Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones," Sustainability, MDPI, vol. 14(5), pages 1-19, March.
    2. Ahalya Prabhakar & Todd Murphey, 2022. "Mechanical intelligence for learning embodied sensor-object relationships," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Andrew M. M. Matheson & Aaron J. Lanz & Ashley M. Medina & Al M. Licata & Timothy A. Currier & Mubarak H. Syed & Katherine I. Nagel, 2022. "A neural circuit for wind-guided olfactory navigation," Nature Communications, Nature, vol. 13(1), pages 1-21, December.

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