{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T08:57:15Z","timestamp":1783501035860,"version":"3.55.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network\u2019s predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers\u2019 perception of complex underwater environments, inspiring advancements in underwater sensing technologies.<\/jats:p>","DOI":"10.3389\/frobt.2023.1231715","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T12:33:20Z","timestamp":1691066000000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Deciphering the connection between upstream obstacles, wake structures, and root signals in seal whisker array sensing using interpretable neural networks"],"prefix":"10.3389","volume":"10","author":[{"given":"Dariush","family":"Bodaghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongfang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/1121776.1121779","article-title":"Challenges for efficient communication in underwater acoustic sensor networks","volume":"1","author":"Akyildiz","year":"2004","journal-title":"ACM Sigbed Rev."},{"key":"B2","first-page":"6836","article-title":"Vivit: A video vision transformer","author":"Arnab","year":"2021"},{"key":"B3","volume-title":"Adaptive input representations for neural language modeling","author":"Baevski","year":"2018"},{"key":"B4","doi-asserted-by":"publisher","first-page":"014012","DOI":"10.1088\/0964-1726\/22\/1\/014012","article-title":"Calibration and validation of a harbor seal whisker-inspired flow sensor","volume":"22","author":"Beam","year":"2013","journal-title":"Smart Mat. 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