{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T14:44:48Z","timestamp":1792680288526,"version":"4.5.0"},"reference-count":42,"publisher":"Wiley","issue":"59","license":[{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A3B2079882"],"award-info":[{"award-number":["2021R1A3B2079882"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Materials"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Conventional artificial vision systems process dynamic scenes inefficiently by reconstructing motion from discrete frames, which requires post\u2010processing. In contrast, real\u2010world environments containing multiple moving objects demand sensor\u2010level discrimination. This work presents a memristive motion\u2010streak neuron that performs spatiotemporal encoding by integrating an Al\/InGaZnO\/Al optomemristor with an Ag\/HfO\n                    <jats:sub>2<\/jats:sub>\n                    \/Pt dynamic memristor, whose relaxation dynamics provide temporal memory. In this system, the presence time of moving objects is detected by decay of the output current, allowing motion direction and speed to be directly inferred from the relaxation behavior. The integrated memristor pixel array enables processing of continuous movements and achieves 96.2% classification accuracy for multiple objects. Also, integrating the motion\u2010streak neuron with the resistor\u2013capacitor kernel further encodes temporal intervals between optical events, enabling recognition of complex movement patterns. This dynamic processing diminishes computational overhead and provides a hardware solution for next\u2010generation vision systems.\n                  <\/jats:p>","DOI":"10.1002\/adma.74793","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T09:19:12Z","timestamp":1787390352000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Memristive Motion\u2010Streak Neuron for Spatiotemporal Multiple Object Detection"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6482-1024","authenticated-orcid":false,"given":"Hyungjun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Materials Science and Engineering and Inter\u2010University Semiconductor Research Center Seoul National University  Seoul Republic of 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