{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T01:07:31Z","timestamp":1775696851527,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["23K11261"],"award-info":[{"award-number":["23K11261"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["JPMJSP2145"],"award-info":[{"award-number":["JPMJSP2145"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["2023020043"],"award-info":[{"award-number":["2023020043"]}]},{"name":"Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING)","award":["23K11261"],"award-info":[{"award-number":["23K11261"]}]},{"name":"Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING)","award":["JPMJSP2145"],"award-info":[{"award-number":["JPMJSP2145"]}]},{"name":"Japan Science and Technology Agency (JST) Support for Pioneering Research Initiated by the Next Generation (SPRING)","award":["2023020043"],"award-info":[{"award-number":["2023020043"]}]},{"name":"Tongji University Support for Outstanding Ph.D Student Short-Term Overseas Research Funding","award":["23K11261"],"award-info":[{"award-number":["23K11261"]}]},{"name":"Tongji University Support for Outstanding Ph.D Student Short-Term Overseas Research Funding","award":["JPMJSP2145"],"award-info":[{"award-number":["JPMJSP2145"]}]},{"name":"Tongji University Support for Outstanding Ph.D Student Short-Term Overseas Research Funding","award":["2023020043"],"award-info":[{"award-number":["2023020043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The complexity and functional evolution of mammalian visual systems have always been a focal point in neuroscience and biological science research. The primary neurons that output motion direction signals have been a focal point of research in visual neuroscience for nearly 130 years. These neurons are widely present in the cortex and retina of mammals. Although the relevant pathways have been discovered and studied for almost 60 years due to experimental accessibility, research still remains at the cellular level. The specific functions and overall operational mechanisms of the component neurons in the motion direction-selective pathways are yet to be clearly elucidated. In this study, we modeled existing relevant neuroscience conclusions based on the symmetry and asymmetry of whole cells in the retina-to-cortex pathway and proposed a quantitative mechanism for motion direction selectivity pathways, called the Artificial Visual System (AVS). By tests based on 1 million instances of 2D, eight-direction grayscale moving objects, including 10 randomly shaped objects of various sizes, we confirm AVS\u2019s high effectiveness on motion direction detecting. Furthermore, by comparing the AVS with two well-known convolutional neural networks, namely LeNet-5 and EfficientNetB0, we verify its efficiency, generalization, and noise resistance. Moreover, the analysis indicates that the AVS exhibits evident biomimetic characteristics and application advantages concerning hardware implementation, biological plausibility, interpretability, parameter count, and learning difficulty.<\/jats:p>","DOI":"10.3390\/sym16121592","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T10:19:42Z","timestamp":1732789182000},"page":"1592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Visual Network with Fully Modeled Retinal Direction-Selective Neural Pathway for Motion Direction Detection in Grayscale Scenes"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9858-4208","authenticated-orcid":false,"given":"Sichen","family":"Tao","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8208-0377","authenticated-orcid":false,"given":"Ruihan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Tongji University, Shanghai 200082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7790-9154","authenticated-orcid":false,"given":"Yifei","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyoshi","family":"Todo","sequence":"additional","affiliation":[{"name":"Hiroyoshi Todo, Wicresoft Co., Ltd., Tokyo 163-0445, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-7444","authenticated-orcid":false,"given":"Zheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7379-1374","authenticated-orcid":false,"given":"Yuki","family":"Todo","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"ref_1","unstructured":"Exner, S. 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