Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Tuning movement for sensing in an uncertain world

View ORCID ProfileChen Chen, Todd D. Murphey, View ORCID ProfileMalcolm A. MacIver
doi: https://doi.org/10.1101/826305
Chen Chen
1Center for Robotics and Biosystems, Northwestern University, Evanston IL
2Department of Biomedical Engineering, Northwestern University, Evanston IL
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Chen Chen
Todd D. Murphey
1Center for Robotics and Biosystems, Northwestern University, Evanston IL
3Department of Mechanical Engineering, Northwestern University, Evanston IL
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Malcolm A. MacIver
1Center for Robotics and Biosystems, Northwestern University, Evanston IL
2Department of Biomedical Engineering, Northwestern University, Evanston IL
3Department of Mechanical Engineering, Northwestern University, Evanston IL
4Department of Neurobiology, Northwestern University, Evanston IL
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Malcolm A. MacIver
  • For correspondence: maciver@northwestern.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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 target tracking trajectories of electric fish. Similarly good agreement was found across three published datasets on visual and olfactory tracking tasks in insects and mammals. 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted June 25, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Tuning movement for sensing in an uncertain world
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Tuning movement for sensing in an uncertain world
Chen Chen, Todd D. Murphey, Malcolm A. MacIver
bioRxiv 826305; doi: https://doi.org/10.1101/826305
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Tuning movement for sensing in an uncertain world
Chen Chen, Todd D. Murphey, Malcolm A. MacIver
bioRxiv 826305; doi: https://doi.org/10.1101/826305

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3598)
  • Biochemistry (7564)
  • Bioengineering (5517)
  • Bioinformatics (20779)
  • Biophysics (10320)
  • Cancer Biology (7973)
  • Cell Biology (11629)
  • Clinical Trials (138)
  • Developmental Biology (6602)
  • Ecology (10198)
  • Epidemiology (2065)
  • Evolutionary Biology (13605)
  • Genetics (9537)
  • Genomics (12843)
  • Immunology (7919)
  • Microbiology (19536)
  • Molecular Biology (7654)
  • Neuroscience (42055)
  • Paleontology (307)
  • Pathology (1257)
  • Pharmacology and Toxicology (2200)
  • Physiology (3266)
  • Plant Biology (7036)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5426)
  • Zoology (1115)