Literature DB >> 27521527

Validating a model for detecting magnetic field intensity using dynamic neural fields.

Brian K Taylor1.   

Abstract

Several animals use properties of Earth's magnetic field as a part of their navigation toolkit to accomplish tasks ranging from local homing to continental migration. Studying these behaviors has led to the postulation of both a magnetite-based sense, and a chemically based radical-pair mechanism. Several researchers have proposed models aimed at both understanding these mechanisms, and offering insights into future physiological experiments. The present work mathematically implements a previously developed conceptual model for sensing and processing magnetite-based magnetosensory feedback by using dynamic neural fields, a computational neuroscience tool for modeling nervous system dynamics and processing. Results demonstrate the plausibility of the conceptual model's predictions. Specifically, a population of magnetoreceptors in which each individual can only sense directional information can encode magnetic intensity en masse. Multiple populations can encode both magnetic direction, and intensity, two parameters that several animals use in their navigational toolkits. This work can be expanded to test other magnetoreceptor models. Published by Elsevier Ltd.

Entities:  

Keywords:  Alternative navigation; Dynamic neural field; Magnetic reception; Magnetoreception; Magnetosensing; Navigation

Mesh:

Substances:

Year:  2016        PMID: 27521527     DOI: 10.1016/j.jtbi.2016.08.010

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  2 in total

1.  Sensation to navigation: a computational neuroscience approach to magnetic field navigation.

Authors:  Sebastian Nichols; Luke Havens; Brian Taylor
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2022-01-31       Impact factor: 1.836

2.  Long-distance transequatorial navigation using sequential measurements of magnetic inclination angle.

Authors:  Brian K Taylor; Kenneth J Lohmann; Luke T Havens; Catherine M F Lohmann; Jesse Granger
Journal:  J R Soc Interface       Date:  2021-01-06       Impact factor: 4.118

  2 in total

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