Spatiotemporal model for depth perception in electric sensing.


Journal

Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342

Informations de publication

Date de publication:
14 01 2019
Historique:
received: 31 07 2018
revised: 24 09 2018
accepted: 08 10 2018
pubmed: 13 10 2018
medline: 24 3 2020
entrez: 13 10 2018
Statut: ppublish

Résumé

Electric sensing involves measuring the voltage changes in an actively generated electric field, enabling an environment to be characterized by its electrical properties. It has been applied in a variety of contexts, from geophysics to biomedical imaging. Some species of fish also use an active electric sense to explore their environment in the dark. One of the primary challenges in such electric sensing involves mapping an environment in three-dimensions using voltage measurements that are limited to a two-dimensional sensor array (i.e. a two-dimensional electric image). In some special cases, the distance of simple objects from the sensor array can be estimated by combining properties of the electric image. Here, we describe a novel algorithm for distance estimation based on a single property of the electric image. Our algorithm can be implemented in two simple ways, involving either different electric field strengths or different sensor thresholds, and is robust to changes in object properties and noise.

Identifiants

pubmed: 30312688
pii: S0022-5193(18)30499-5
doi: 10.1016/j.jtbi.2018.10.023
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

157-169

Informations de copyright

Copyright © 2018 Elsevier Ltd. All rights reserved.

Auteurs

Babak Pourziaei (B)

Department of Mathematics & Statistics, York University, Toronto M3J 1P3, Canada.

Gregory M Lewis (GM)

Faculty of Science, University of Ontario Institute of Technology, Oshawa L1G 0C5, Canada. Electronic address: Greg.Lewis@uoit.ca.

Huaxiong Huang (H)

Department of Mathematics & Statistics, York University, Toronto M3J 1P3, Canada; The Centre for Quantitative Analysis and Modeling, Fields Institute, Toronto, ON, M5T 3J1, Canada.

John E Lewis (JE)

Department of Biology, University of Ottawa, Ottawa K1N 6N5, Canada; University of Ottawa Brain and Mind Research Institute, Ottawa K1H 8M5, Canada.

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Classifications MeSH