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Tuning movement for sensing in an uncertain world.

Authors
  • Chen, Chen1, 2
  • Murphey, Todd D1, 3
  • MacIver, Malcolm A1, 2, 3, 4
  • 1 Center for Robotics and Biosystems, Northwestern University, Evanston, United States. , (United States)
  • 2 Department of Biomedical Engineering, Northwestern University, Evanston, United States. , (United States)
  • 3 Department of Mechanical Engineering, Northwestern University, Evanston, United States. , (United States)
  • 4 Department of Neurobiology, Northwestern University, Evanston, United States. , (United States)
Type
Published Article
Journal
eLife
Publisher
"eLife Sciences Organisation, Ltd."
Publication Date
Sep 22, 2020
Volume
9
Identifiers
DOI: 10.7554/eLife.52371
PMID: 32959777
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist-in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering-predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement's predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance. © 2020, Chen et al.

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