{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T09:48:39Z","timestamp":1762076919429,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"TUBITAK","doi-asserted-by":"publisher","award":["114E583"],"award-info":[{"award-number":["114E583"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Software Certification Research Center","award":["114E583"],"award-info":[{"award-number":["114E583"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy\u2013entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced\u2014seconds versus hours\u2014encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.<\/jats:p>","DOI":"10.3390\/e24111678","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T03:31:10Z","timestamp":1668742270000},"page":"1678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3554-7922","authenticated-orcid":false,"given":"Meric","family":"Yucel","sequence":"first","affiliation":[{"name":"National Software Certification Research Center, Istanbul Technical University, Istanbul 34469, Turkey"},{"name":"Computer Engineering Department, Istanbul University-Cerrahpasa, Istanbul 34320, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3985-4065","authenticated-orcid":false,"given":"Serdar","family":"Bagis","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-1211","authenticated-orcid":false,"given":"Ahmet","family":"Sertbas","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Istanbul University-Cerrahpasa, Istanbul 34320, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3856-6360","authenticated-orcid":false,"given":"Mehmet","family":"Sarikaya","sequence":"additional","affiliation":[{"name":"Materials Science & Engineering Department, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-9434","authenticated-orcid":false,"given":"Burak","family":"Ustundag","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MASSP.1984.1162229","article-title":"Vector quantization","volume":"1","author":"Gray","year":"1984","journal-title":"IEEE Assp. 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