{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T22:55:53Z","timestamp":1777157753348,"version":"3.51.4"},"reference-count":28,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Neuromorph. Comput. 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Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1 \u00d7 104 pJ (0.3% of conventional computing requirements). The results demonstrate the system\u2019s adjustment to changing work environments and its efficient use of energy resources, with a moderate increase in energy consumption of 37% for dynamic obstacles, compared to non-obstacle scenarios.<\/jats:p>","DOI":"10.1088\/2634-4386\/ad8c79","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T22:54:38Z","timestamp":1730242478000},"page":"044004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A cloud-edge framework for energy-efficient event-driven control: an integration of online supervised learning, spiking neural networks and local plasticity rules"],"prefix":"10.1088","volume":"4","author":[{"given":"Reza","family":"Ahmadvand","sequence":"first","affiliation":[]},{"given":"Sarah Safura","family":"Sharif","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7339-810X","authenticated-orcid":true,"given":"Yaser Mike","family":"Banad","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"ncead8c79bib1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s43588-021-00184-y","article-title":"Opportunities for neuromorphic computing algorithms and applications","volume":"2","author":"Schuman","year":"2022","journal-title":"Nat. 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