4.6 Article

Active head rolls enhance sonar-based auditory localization performance

期刊

PLOS COMPUTATIONAL BIOLOGY
卷 17, 期 5, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1008973

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资金

  1. Hong Kong Research Grants Council [16213617]
  2. Comparative and Evolutionary Biology of Hearing institutional training grant from the National Institute of Deafness and Communicative Disorders of the National Institutes of Health [T32 DC000046]
  3. NSF [IBN-0111973, IOS-1460149]
  4. AFOSR [FA9550-14-1039]
  5. ONR [N00014-12-1-0339]

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The study demonstrates that big brown bats utilize active head movements during echolocation, resulting in improved target localization accuracy and reduced time required for localization.
Animals utilize a variety of active sensing mechanisms to perceive the world around them. Echolocating bats are an excellent model for the study of active auditory localization. The big brown bat (Eptesicus fuscus), for instance, employs active head roll movements during sonar prey tracking. The function of head rolls in sound source localization is not well understood. Here, we propose an echolocation model with multi-axis head rotation to investigate the effect of active head roll movements on sound localization performance. The model autonomously learns to align the bat's head direction towards the target. We show that a model with active head roll movements better localizes targets than a model without head rolls. Furthermore, we demonstrate that active head rolls also reduce the time required for localization in elevation. Finally, our model offers key insights to sound localization cues used by echolocating bats employing active head movements during echolocation. Author summary Active sensing is a crucial aspect of an echolocating bat's auditory spatial perception. Head and ear movements frequently accompany their sonar call production and reception. The big brown bat waggles its head while it tracks the position of insects in darkness; however the role of these movements is not well understood. We addressed this question using a computational model that simulates active head rotations that resemble those reported in big brown bats. Our model autonomously learns to localize targets by actively rotating the head direction towards the target. We discovered that the active head waggles improve the localization accuracy, particularly in the vertical dimension.

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