{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T09:54:48Z","timestamp":1791453288976,"version":"4.3.3"},"reference-count":0,"publisher":"Project MUSE","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jhi"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p xml:lang=\"en\">Abstract: This paper examines the origins of the concept of \"deep learning,\" and traces it to its origins in the ideas of the psychologist Donald Olding Hebb. In the 1930s, Hebb formulated a theory of brain function that was meant to explain the surprising intelligence scores of brain surgery patients. This paper then traces how Hebb's cell assembly theory found its way into the earliest attempts by the IBM engineer Nathanial Rochester to engineer a learning computer in the 1950s. Finally, I examined the legacy of this encounter between neurophysiology and engineering for modern artificial intelligence and neural networking systems.<\/jats:p>","DOI":"10.1353\/jhi.2026.a1003011","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T09:34:36Z","timestamp":1791452076000},"page":"769-790","source":"Crossref","is-referenced-by-count":0,"title":["\"Engineering Sense\": Donald Hebb, Nathaniel Rochester, and the Origins of Deep Learning"],"prefix":"10.1353","volume":"87","author":[{"given":"Yvan","family":"Prkachin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"147","container-title":["Journal of the History of Ideas"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T09:34:38Z","timestamp":1791452078000},"score":1,"resource":{"primary":{"URL":"https:\/\/muse.jhu.edu\/article\/1003011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,10]]}},"URL":"https:\/\/doi.org\/10.1353\/jhi.2026.a1003011","relation":{},"ISSN":["1086-3222"],"issn-type":[{"value":"1086-3222","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]}}}