{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:11:35Z","timestamp":1767175895863,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012004","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000}}],"reference-count":70,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel science foundation","doi-asserted-by":"publisher","award":["552\/19"],"award-info":[{"award-number":["552\/19"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001658","name":"Minerva Foundation","doi-asserted-by":"publisher","award":["Center for Lab Evolution"],"award-info":[{"award-number":["Center for Lab Evolution"]}],"id":[{"id":"10.13039\/501100001658","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008536","name":"Amazon Web Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008536","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007065","name":"Nvidia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the\n                    <jats:italic>self-replicating artificial neural network<\/jats:italic>\n                    (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012004","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T14:22:38Z","timestamp":1711635758000},"page":"e1012004","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-replicating artificial neural networks give rise to universal evolutionary dynamics"],"prefix":"10.1371","volume":"20","author":[{"given":"Boaz","family":"Shvartzman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9653-4458","authenticated-orcid":true,"given":"Yoav","family":"Ram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"pcbi.1012004.ref001","volume-title":"The Genetical Theory of Natural Selection","author":"RA Fisher","year":"1958"},{"key":"pcbi.1012004.ref002","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1162\/106454604773563612","article-title":"Avida: A Software Platform for Research in Computational Evolutionary Biology.","volume":"10","author":"C Ofria","year":"2004","journal-title":"Artificial Life"},{"key":"pcbi.1012004.ref003","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.jtbi.2006.09.005","article-title":"Evolutionary coupling between the deleteriousness of gene mutations and the amount of non-coding sequences","volume":"244","author":"C Knibbe","year":"2007","journal-title":"Journal of Theoretical Biology"},{"key":"pcbi.1012004.ref004","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s42256-018-0006-z","article-title":"Designing neural networks through neuroevolution","volume":"1","author":"KO Stanley","year":"2019","journal-title":"Nature Machine Intelligence"},{"key":"pcbi.1012004.ref005","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1007\/978-0-387-30164-8_589","volume-title":"Encyclopedia of Machine Learning","author":"R Miikkulainen","year":"2011"},{"key":"pcbi.1012004.ref006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1745-6150-8-24","article-title":"Interaction-based evolution: How natural selection and nonrandom mutation work together","volume":"8","author":"A. 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