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A Real-World Implementation of Neurally-Based Magnetic Reception and Navigation

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Biomimetic and Biohybrid Systems (Living Machines 2022)

Abstract

The Earth’s magnetic field provides a signal that animals and man-made systems can leverage for navigation across long-distances (e.g., continents and oceans). Despite ongoing research in animal magnetoreception, the underlying magnetoreceptors, neural processing, and strategies that ultimately drive behavior are still not well understood. One hypothesis is that animals might use unique or rare combinations of magnetic field properties as a magnetic signature to denote specific locations or regions. While biological observations have provided evidence of this navigation strategy, both the strategy and its underpinnings remain unconfirmed. Software simulations have demonstrated the feasibility of this type of navigation strategy, and a nervous system’s ability to execute it. However, software simulations are limited in the phenomena they are able to properly capture. In this study, expanding on previous work, we implement a magnetic signatures-based navigation strategy in a laboratory-based robotic system. The system consists of a robotic rotary motion platform that serves as a proxy for a navigating animal, a magnetometer for magnetic field sensing, and a computer-controlled magnetic coil system. The system uses neural fields to process magnetic information in a neurally relevant way. The findings demonstrate that nervous system-based processing can successfully enable navigation using magnetic signatures. This lends further credence to the concept of animals using magnetic signatures to navigate, and can provide inspiration for the development of novel man-made navigation and sensor-fusion systems.

Supported in part by a grant from the Air Force Office of Scientific Research (FA9550-20-1-0399).

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Correspondence to Brian K. Taylor .

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Harvey, A., Taylor, B.K. (2022). A Real-World Implementation of Neurally-Based Magnetic Reception and Navigation. In: Hunt, A., et al. Biomimetic and Biohybrid Systems. Living Machines 2022. Lecture Notes in Computer Science(), vol 13548. Springer, Cham. https://doi.org/10.1007/978-3-031-20470-8_22

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  • DOI: https://doi.org/10.1007/978-3-031-20470-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20469-2

  • Online ISBN: 978-3-031-20470-8

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