{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:58:20Z","timestamp":1772117900680,"version":"3.50.1"},"reference-count":58,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11925103"],"award-info":[{"award-number":["11925103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:p>\n                    Detecting looming signals for collision avoidance encounters challenges in real\u2010world scenarios, where moving backgrounds can interfere as an agent navigates through complex natural environments. Remarkably, even insects with limited neural systems adeptly respond to looming stimuli while in motion at high speeds. Existing insect\u2010inspired looming detection models typically rely on either motion\u2010pathway or feature\u2010pathway signals, yet both are susceptible to dynamic visual scene interference. Coordinating interneuron signals from both pathways can enhance the looming detection performance under dynamic conditions. An artificial neural network is employed to construct a combined\u2010pathway model based on\n                    <jats:italic>Drosophila<\/jats:italic>\n                    anatomy. The model outperforms state\u2010of\u2010the\u2010art bio\u2010inspired looming\u2010detection models in tasks involving dynamic backgrounds, simulated by animated 2D\u2010moving natural scenes or recorded in reality when an unmanned aerial vehicle performs obstacle collision avoidance tasks. Notably, by combining neural anatomy architecture and appropriate multiobjective tasks, the model exhibits convergent neural dynamics with biological counterparts post\u2010training, offering network explanations and mechanistic insights. Specifically, a multiplicative interneuron operation enhances looming signal patterns and reduces background interferences, generalizing to more complex scenarios, such as AirSim 3D environments and real\u2010world situations. The work introduces testable biological hypotheses and a promising bioinspired solution for looming detection in dynamic visual environments.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202400198","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:15:41Z","timestamp":1722212141000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways"],"prefix":"10.1002","volume":"6","author":[{"given":"Bo","family":"Gu","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain\u2010Inspired Intelligence Fudan University  Shanghai 200433 China"},{"name":"Key Laboratory of Computational Neuroscience and Brain\u2010Inspired Intelligence, Ministry of Education Fudan University  Shanghai 200433 China"}]},{"given":"Jianfeng","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain\u2010Inspired Intelligence Fudan University  Shanghai 200433 China"},{"name":"Key Laboratory of Computational Neuroscience and Brain\u2010Inspired Intelligence, Ministry of Education Fudan University  Shanghai 200433 China"},{"name":"MOE Frontiers Center for Brain Science Fudan University  Shanghai 200433 China"},{"name":"Zhangjiang Fudan International Innovation Center  Shanghai 201203 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9991-4053","authenticated-orcid":false,"given":"Zhuoyi","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain\u2010Inspired Intelligence Fudan University  Shanghai 200433 China"},{"name":"Key Laboratory of Computational Neuroscience and Brain\u2010Inspired Intelligence, Ministry of Education Fudan University  Shanghai 200433 China"},{"name":"MOE Frontiers Center for Brain Science Fudan University  Shanghai 200433 China"},{"name":"Zhangjiang Fudan International Innovation Center  Shanghai 201203 China"}]}],"member":"311","published-online":{"date-parts":[[2024,7,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1248955"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.873286"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2946090"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace3030021"},{"key":"e_1_2_10_6_1","first-page":"703","volume":"46","author":"Serres J. 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