{"id":114148,"date":"2025-09-04T14:21:21","date_gmt":"2025-09-04T14:21:21","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/114148\/"},"modified":"2025-09-04T14:21:21","modified_gmt":"2025-09-04T14:21:21","slug":"training-of-physical-neural-networks","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/114148\/","title":{"rendered":"Training of physical neural networks"},"content":{"rendered":"<p class=\"c-article-references__text\" id=\"ref-CR1\">Samborska, V. Scaling up: how increasing inputs has made artificial intelligence more capable. Our World in Data <a href=\"https:\/\/ourworldindata.org\/scaling-up-ai\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/ourworldindata.org\/scaling-up-ai\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/ourworldindata.org\/scaling-up-ai<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. &amp; Eleftheriou, E. Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529\u2013544 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41565-020-0655-z\" data-track-item_id=\"10.1038\/s41565-020-0655-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41565-020-0655-z\" aria-label=\"Article reference 2\" data-doi=\"10.1038\/s41565-020-0655-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2020NatNa..15..529S\" aria-label=\"ADS reference 2\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXlvFyltrY%3D\" aria-label=\"CAS reference 2\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32231270\" aria-label=\"PubMed reference 2\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 2\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Memory%20devices%20and%20applications%20for%20in-memory%20computing&amp;journal=Nat.%20Nanotechnol.&amp;doi=10.1038%2Fs41565-020-0655-z&amp;volume=15&amp;pages=529-544&amp;publication_year=2020&amp;author=Sebastian%2CA&amp;author=Gallo%2CM&amp;author=Khaddam-Aljameh%2CR&amp;author=Eleftheriou%2CE\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39\u201347 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41586-020-2973-6\" data-track-item_id=\"10.1038\/s41586-020-2973-6\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41586-020-2973-6\" aria-label=\"Article reference 3\" data-doi=\"10.1038\/s41586-020-2973-6\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2020Natur.588...39W\" aria-label=\"ADS reference 3\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXisVOks7rI\" aria-label=\"CAS reference 3\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33268862\" aria-label=\"PubMed reference 3\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 3\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Inference%20in%20artificial%20intelligence%20with%20deep%20optics%20and%20photonics&amp;journal=Nature&amp;doi=10.1038%2Fs41586-020-2973-6&amp;volume=588&amp;pages=39-47&amp;publication_year=2020&amp;author=Wetzstein%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Wright, L. G. et al. Deep physical neural networks trained with backpropagation. Nature 601, 549\u2013555 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41586-021-04223-6\" data-track-item_id=\"10.1038\/s41586-021-04223-6\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41586-021-04223-6\" aria-label=\"Article reference 4\" data-doi=\"10.1038\/s41586-021-04223-6\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022Natur.601..549W\" aria-label=\"ADS reference 4\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XitFGlurY%3D\" aria-label=\"CAS reference 4\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35082422\" aria-label=\"PubMed reference 4\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8791835\" aria-label=\"PubMed Central reference 4\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 4\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20physical%20neural%20networks%20trained%20with%20backpropagation&amp;journal=Nature&amp;doi=10.1038%2Fs41586-021-04223-6&amp;volume=601&amp;pages=549-555&amp;publication_year=2022&amp;author=Wright%2CLG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100\u2013123 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.neunet.2019.03.005\" data-track-item_id=\"10.1016\/j.neunet.2019.03.005\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.neunet.2019.03.005\" aria-label=\"Article reference 5\" data-doi=\"10.1016\/j.neunet.2019.03.005\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=30981085\" aria-label=\"PubMed reference 5\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 5\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Recent%20advances%20in%20physical%20reservoir%20computing%3A%20a%20review&amp;journal=Neural%20Netw.&amp;doi=10.1016%2Fj.neunet.2019.03.005&amp;volume=115&amp;pages=100-123&amp;publication_year=2019&amp;author=Tanaka%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Hughes, T. W., Williamson, I. A., Minkov, M. &amp; Fan, S. Wave physics as an analog recurrent neural network. Sci. Adv. 5, eaay6946 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/sciadv.aay6946\" data-track-item_id=\"10.1126\/sciadv.aay6946\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fsciadv.aay6946\" aria-label=\"Article reference 6\" data-doi=\"10.1126\/sciadv.aay6946\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2019SciA....5.6946H\" aria-label=\"ADS reference 6\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=31903420\" aria-label=\"PubMed reference 6\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6924985\" aria-label=\"PubMed Central reference 6\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 6\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Wave%20physics%20as%20an%20analog%20recurrent%20neural%20network&amp;journal=Sci.%20Adv.&amp;doi=10.1126%2Fsciadv.aay6946&amp;volume=5&amp;publication_year=2019&amp;author=Hughes%2CTW&amp;author=Williamson%2CIA&amp;author=Minkov%2CM&amp;author=Fan%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">Onodera, T. et al. Scaling on-chip photonic neural processors using arbitrarily programmable wave propagation. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2402.17750\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2402.17750\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2402.17750<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Momeni, A., Rahmani, B., Mall\u00e9jac, M., del Hougne, P. &amp; Fleury, R. Backpropagation-free training of deep physical neural networks. Science 382, 1297\u20131303 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.adi8474\" data-track-item_id=\"10.1126\/science.adi8474\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.adi8474\" aria-label=\"Article reference 8\" data-doi=\"10.1126\/science.adi8474\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023Sci...382.1297M\" aria-label=\"ADS reference 8\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=4683727\" aria-label=\"MathSciNet reference 8\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXis1aqtLvO\" aria-label=\"CAS reference 8\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37995209\" aria-label=\"PubMed reference 8\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 8\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Backpropagation-free%20training%20of%20deep%20physical%20neural%20networks&amp;journal=Science&amp;doi=10.1126%2Fscience.adi8474&amp;volume=382&amp;pages=1297-1303&amp;publication_year=2023&amp;author=Momeni%2CA&amp;author=Rahmani%2CB&amp;author=Mall%C3%A9jac%2CM&amp;author=Hougne%2CP&amp;author=Fleury%2CR\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Xu, Z. et al. Large-scale photonic chiplet Taichi empowers 160-TOPS\/W artificial general intelligence. Science 384, 202\u2013209 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.adl1203\" data-track-item_id=\"10.1126\/science.adl1203\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.adl1203\" aria-label=\"Article reference 9\" data-doi=\"10.1126\/science.adl1203\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024Sci...384..202X\" aria-label=\"ADS reference 9\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXotFCnt7o%3D\" aria-label=\"CAS reference 9\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38603505\" aria-label=\"PubMed reference 9\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 9\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20photonic%20chiplet%20Taichi%20empowers%20160-TOPS%2FW%20artificial%20general%20intelligence&amp;journal=Science&amp;doi=10.1126%2Fscience.adl1203&amp;volume=384&amp;pages=202-209&amp;publication_year=2024&amp;author=Xu%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Rumelhart, D. E., Hinton, G. E. &amp; Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533\u2013536 (1986).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/323533a0\" data-track-item_id=\"10.1038\/323533a0\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2F323533a0\" aria-label=\"Article reference 10\" data-doi=\"10.1038\/323533a0\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=1986Natur.323..533R\" aria-label=\"ADS reference 10\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 10\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Learning%20representations%20by%20back-propagating%20errors&amp;journal=Nature&amp;doi=10.1038%2F323533a0&amp;volume=323&amp;pages=533-536&amp;publication_year=1986&amp;author=Rumelhart%2CDE&amp;author=Hinton%2CGE&amp;author=Williams%2CRJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004\u20131008 (2018).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.aat8084\" data-track-item_id=\"10.1126\/science.aat8084\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.aat8084\" aria-label=\"Article reference 11\" data-doi=\"10.1126\/science.aat8084\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2018Sci...361.1004L\" aria-label=\"ADS reference 11\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=3837095\" aria-label=\"MathSciNet reference 11\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC1cXhs1ChsLfJ\" aria-label=\"CAS reference 11\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=30049787\" aria-label=\"PubMed reference 11\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 11\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=All-optical%20machine%20learning%20using%20diffractive%20deep%20neural%20networks&amp;journal=Science&amp;doi=10.1126%2Fscience.aat8084&amp;volume=361&amp;pages=1004-1008&amp;publication_year=2018&amp;author=Lin%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Le Gallo, M. et al. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron. 6, 680\u2013693 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41928-023-01010-1\" data-track-item_id=\"10.1038\/s41928-023-01010-1\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41928-023-01010-1\" aria-label=\"Article reference 12\" data-doi=\"10.1038\/s41928-023-01010-1\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 12\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%2064-core%20mixed-signal%20in-memory%20compute%20chip%20based%20on%20phase-change%20memory%20for%20deep%20neural%20network%20inference&amp;journal=Nat.%20Electron.&amp;doi=10.1038%2Fs41928-023-01010-1&amp;volume=6&amp;pages=680-693&amp;publication_year=2023&amp;author=Gallo%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Chen, Z. et al. Deep learning with coherent VCSEL neural networks. Nat. Photon. 17, 723\u2013730 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41566-023-01233-w\" data-track-item_id=\"10.1038\/s41566-023-01233-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41566-023-01233-w\" aria-label=\"Article reference 13\" data-doi=\"10.1038\/s41566-023-01233-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NaPho..17..723C\" aria-label=\"ADS reference 13\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXhsFWisbjF\" aria-label=\"CAS reference 13\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 13\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20learning%20with%20coherent%20VCSEL%20neural%20networks&amp;journal=Nat.%20Photon.&amp;doi=10.1038%2Fs41566-023-01233-w&amp;volume=17&amp;pages=723-730&amp;publication_year=2023&amp;author=Chen%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Mengu, D. et al. Misalignment resilient diffractive optical networks. Nanophotonics 9, 4207\u20134219 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1515\/nanoph-2020-0291\" data-track-item_id=\"10.1515\/nanoph-2020-0291\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1515%2Fnanoph-2020-0291\" aria-label=\"Article reference 14\" data-doi=\"10.1515\/nanoph-2020-0291\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 14\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Misalignment%20resilient%20diffractive%20optical%20networks&amp;journal=Nanophotonics&amp;doi=10.1515%2Fnanoph-2020-0291&amp;volume=9&amp;pages=4207-4219&amp;publication_year=2020&amp;author=Mengu%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Matsushima, K. &amp; Shimobaba, T. Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields. Opt. Express 17, 19662\u201319673 (2009).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/OE.17.019662\" data-track-item_id=\"10.1364\/OE.17.019662\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FOE.17.019662\" aria-label=\"Article reference 15\" data-doi=\"10.1364\/OE.17.019662\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2009OExpr..1719662M\" aria-label=\"ADS reference 15\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD1MXhtlGmsr%2FF\" aria-label=\"CAS reference 15\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=19997186\" aria-label=\"PubMed reference 15\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 15\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Band-limited%20angular%20spectrum%20method%20for%20numerical%20simulation%20of%20free-space%20propagation%20in%20far%20and%20near%20fields&amp;journal=Opt.%20Express&amp;doi=10.1364%2FOE.17.019662&amp;volume=17&amp;pages=19662-19673&amp;publication_year=2009&amp;author=Matsushima%2CK&amp;author=Shimobaba%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Launay, J., Poli, I., Boniface, F. &amp; Krzakala, F. Direct feedback alignment scales to modern deep learning tasks and architectures. Adv. Neural Inf. Process. Syst. 33, 9346\u20139360 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 16\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Direct%20feedback%20alignment%20scales%20to%20modern%20deep%20learning%20tasks%20and%20architectures&amp;journal=Adv.%20Neural%20Inf.%20Process.%20Syst.&amp;volume=33&amp;pages=9346-9360&amp;publication_year=2020&amp;author=Launay%2CJ&amp;author=Poli%2CI&amp;author=Boniface%2CF&amp;author=Krzakala%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Cramer, B. et al. Surrogate gradients for analog neuromorphic computing. Proc. Natl Acad. Sci. 119, e2109194119 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1073\/pnas.2109194119\" data-track-item_id=\"10.1073\/pnas.2109194119\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1073%2Fpnas.2109194119\" aria-label=\"Article reference 17\" data-doi=\"10.1073\/pnas.2109194119\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=4409541\" aria-label=\"MathSciNet reference 17\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35042792\" aria-label=\"PubMed reference 17\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8794842\" aria-label=\"PubMed Central reference 17\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 17\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Surrogate%20gradients%20for%20analog%20neuromorphic%20computing&amp;journal=Proc.%20Natl%20Acad.%20Sci.&amp;doi=10.1073%2Fpnas.2109194119&amp;volume=119&amp;publication_year=2022&amp;author=Cramer%2CB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Spall, J., Guo, X. &amp; Lvovsky, A. I. Hybrid training of optical neural networks. Optica 9, 803\u2013811 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/OPTICA.456108\" data-track-item_id=\"10.1364\/OPTICA.456108\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FOPTICA.456108\" aria-label=\"Article reference 18\" data-doi=\"10.1364\/OPTICA.456108\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022Optic...9..803S\" aria-label=\"ADS reference 18\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 18\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Hybrid%20training%20of%20optical%20neural%20networks&amp;journal=Optica&amp;doi=10.1364%2FOPTICA.456108&amp;volume=9&amp;pages=803-811&amp;publication_year=2022&amp;author=Spall%2CJ&amp;author=Guo%2CX&amp;author=Lvovsky%2CAI\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Lillicrap, T. P., Cownden, D., Tweed, D. B. &amp; Akerman, C. J. Random synaptic feedback weights support error backpropagation for deep learning. Nat. Commun. 7, 13276 (2016).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/ncomms13276\" data-track-item_id=\"10.1038\/ncomms13276\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fncomms13276\" aria-label=\"Article reference 19\" data-doi=\"10.1038\/ncomms13276\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2016NatCo...713276L\" aria-label=\"ADS reference 19\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC28XhvVehtLrM\" aria-label=\"CAS reference 19\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=27824044\" aria-label=\"PubMed reference 19\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5105169\" aria-label=\"PubMed Central reference 19\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 19\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Random%20synaptic%20feedback%20weights%20support%20error%20backpropagation%20for%20deep%20learning&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fncomms13276&amp;volume=7&amp;publication_year=2016&amp;author=Lillicrap%2CTP&amp;author=Cownden%2CD&amp;author=Tweed%2CDB&amp;author=Akerman%2CCJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Brunton, S. L. &amp; Kutz, J. N. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Cambridge Univ. Press, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Hinton, G. The forward-forward algorithm: some preliminary investigations. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2212.13345\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2212.13345\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2212.13345<\/a> (2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Laydevant, J., Lott, A., Venturelli, D. &amp; McMahon, P. L. The benefits of self-supervised learning for training physical neural networks. In Proc. 37th First Workshop on Machine Learning with New Compute Paradigms at NeurIPS 2023 (MLNPCP 2023) <a href=\"https:\/\/openreview.net\/forum?id=Fik4cO7FXd\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/openreview.net\/forum?id=Fik4cO7FXd\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/openreview.net\/forum?id=Fik4cO7FXd<\/a> (OpenReview, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Refinetti, M., d\u2019Ascoli, S., Ohana, R. &amp; Goldt, S. Align, then memorise: the dynamics of learning with feedback alignment. In Proc. 38th International Conference on Machine Learning, 8925\u20138935 (MLR Press, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Lillicrap, T. P., Cownden, D., Tweed, D. B. &amp; Akerman, C. J. Random feedback weights support learning in deep neural networks. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1411.0247\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/1411.0247\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/1411.0247<\/a> (2014).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Launay, J. et al. Hardware beyond backpropagation: a photonic co-processor for direct feedback alignment. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2012.06373\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2012.06373\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2012.06373<\/a> (2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Nakajima, M. et al. Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware. Nat. Commun. 13, 7847 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-022-35216-2\" data-track-item_id=\"10.1038\/s41467-022-35216-2\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-022-35216-2\" aria-label=\"Article reference 26\" data-doi=\"10.1038\/s41467-022-35216-2\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022NatCo..13.7847N\" aria-label=\"ADS reference 26\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XjtF2nsr%2FP\" aria-label=\"CAS reference 26\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36572696\" aria-label=\"PubMed reference 26\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9792515\" aria-label=\"PubMed Central reference 26\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 26\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Physical%20deep%20learning%20with%20biologically%20inspired%20training%20method%3A%20gradient-free%20approach%20for%20physical%20hardware&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-022-35216-2&amp;volume=13&amp;publication_year=2022&amp;author=Nakajima%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Hinton, G. E., Dayan, P., Frey, B. J. &amp; Neal, R. M. The \u201cwake-sleep\u201d algorithm for unsupervised neural networks. Science 268, 1158\u20131161 (1995).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.7761831\" data-track-item_id=\"10.1126\/science.7761831\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.7761831\" aria-label=\"Article reference 27\" data-doi=\"10.1126\/science.7761831\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=1995Sci...268.1158H\" aria-label=\"ADS reference 27\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DyaK2MXlvVyqs7w%3D\" aria-label=\"CAS reference 27\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=7761831\" aria-label=\"PubMed reference 27\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 27\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20%E2%80%9Cwake-sleep%E2%80%9D%20algorithm%20for%20unsupervised%20neural%20networks&amp;journal=Science&amp;doi=10.1126%2Fscience.7761831&amp;volume=268&amp;pages=1158-1161&amp;publication_year=1995&amp;author=Hinton%2CGE&amp;author=Dayan%2CP&amp;author=Frey%2CBJ&amp;author=Neal%2CRM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">L\u00f6we, S., O\u2019Connor, P. &amp; Veeling, B. Putting an end to end-to-end: gradient-isolated learning of representations. In Proc. Advances in Neural Information Processing Systems 32 (NeuroIPS 2019), 3039\u20133051 (ACM, 2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">N\u00f8kland, A. &amp; Eidnes, L. H. Training neural networks with local error signals. In Proc. 36th International Conference on Machine Learning, 4839\u20134850 (MLR Press, 2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">Siddiqui, S. A., Krueger, D., LeCun, Y. &amp; Deny, S. Blockwise self-supervised learning at scale. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2302.01647v1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2302.01647v1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2302.01647v1<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Oguz, I. et al. Forward\u2013forward training of an optical neural network. Opt. Lett. 48, 5249\u20135252 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/OL.496884\" data-track-item_id=\"10.1364\/OL.496884\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FOL.496884\" aria-label=\"Article reference 31\" data-doi=\"10.1364\/OL.496884\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023OptL...48.5249O\" aria-label=\"ADS reference 31\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37831839\" aria-label=\"PubMed reference 31\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 31\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Forward%E2%80%93forward%20training%20of%20an%20optical%20neural%20network&amp;journal=Opt.%20Lett.&amp;doi=10.1364%2FOL.496884&amp;volume=48&amp;pages=5249-5252&amp;publication_year=2023&amp;author=Oguz%2CI\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">Xue, Z. et al. Fully forward mode training for optical neural networks. Nature 632, 280\u2013286 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41586-024-07687-4\" data-track-item_id=\"10.1038\/s41586-024-07687-4\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41586-024-07687-4\" aria-label=\"Article reference 32\" data-doi=\"10.1038\/s41586-024-07687-4\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhslWktrrF\" aria-label=\"CAS reference 32\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=39112621\" aria-label=\"PubMed reference 32\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11306102\" aria-label=\"PubMed Central reference 32\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 32\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fully%20forward%20mode%20training%20for%20optical%20neural%20networks&amp;journal=Nature&amp;doi=10.1038%2Fs41586-024-07687-4&amp;volume=632&amp;pages=280-286&amp;publication_year=2024&amp;author=Xue%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR33\">Spall, J. C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Autom. Control 37, 332\u2013341 (1992).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/9.119632\" data-track-item_id=\"10.1109\/9.119632\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2F9.119632\" aria-label=\"Article reference 33\" data-doi=\"10.1109\/9.119632\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=1148715\" aria-label=\"MathSciNet reference 33\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 33\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Multivariate%20stochastic%20approximation%20using%20a%20simultaneous%20perturbation%20gradient%20approximation&amp;journal=IEEE%20Trans.%20Autom.%20Control&amp;doi=10.1109%2F9.119632&amp;volume=37&amp;pages=332-341&amp;publication_year=1992&amp;author=Spall%2CJC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">McCaughan, A. N. et al. Multiplexed gradient descent: fast online training of modern datasets on hardware neural networks without backpropagation. APL Mach. Learn. 1, 026118 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1063\/5.0157645\" data-track-item_id=\"10.1063\/5.0157645\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1063%2F5.0157645\" aria-label=\"Article reference 34\" data-doi=\"10.1063\/5.0157645\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 34\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Multiplexed%20gradient%20descent%3A%20fast%20online%20training%20of%20modern%20datasets%20on%20hardware%20neural%20networks%20without%20backpropagation&amp;journal=APL%20Mach.%20Learn.&amp;doi=10.1063%2F5.0157645&amp;volume=1&amp;publication_year=2023&amp;author=McCaughan%2CAN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">Bandyopadhyay, S. et al. Single-chip photonic deep neural network with forward-only training. Nat. Photon. 18, 1335\u20131343 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41566-024-01567-z\" data-track-item_id=\"10.1038\/s41566-024-01567-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41566-024-01567-z\" aria-label=\"Article reference 35\" data-doi=\"10.1038\/s41566-024-01567-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXis1ygsr%2FF\" aria-label=\"CAS reference 35\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 35\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Single-chip%20photonic%20deep%20neural%20network%20with%20forward-only%20training&amp;journal=Nat.%20Photon.&amp;doi=10.1038%2Fs41566-024-01567-z&amp;volume=18&amp;pages=1335-1343&amp;publication_year=2024&amp;author=Bandyopadhyay%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Oguz, I. et al. Programming nonlinear propagation for efficient optical learning machines. Adv. Photonics 6, 016002 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1117\/1.AP.6.1.016002\" data-track-item_id=\"10.1117\/1.AP.6.1.016002\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1117%2F1.AP.6.1.016002\" aria-label=\"Article reference 36\" data-doi=\"10.1117\/1.AP.6.1.016002\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024AdPho...6a6002O\" aria-label=\"ADS reference 36\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXhtF2iu7jK\" aria-label=\"CAS reference 36\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 36\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Programming%20nonlinear%20propagation%20for%20efficient%20optical%20learning%20machines&amp;journal=Adv.%20Photonics&amp;doi=10.1117%2F1.AP.6.1.016002&amp;volume=6&amp;publication_year=2024&amp;author=Oguz%2CI\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Skalli, A. et al. Annealing-inspired training of an optical neural network with ternary weights. Commun. Phys. 8, 68 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s42005-025-01972-y\" data-track-item_id=\"10.1038\/s42005-025-01972-y\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs42005-025-01972-y\" aria-label=\"Article reference 37\" data-doi=\"10.1038\/s42005-025-01972-y\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 37\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Annealing-inspired%20training%20of%20an%20optical%20neural%20network%20with%20ternary%20weights&amp;journal=Commun.%20Phys.&amp;doi=10.1038%2Fs42005-025-01972-y&amp;volume=8&amp;publication_year=2025&amp;author=Skalli%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Bueno, J. et al. Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5, 756\u2013760 (2018).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/OPTICA.5.000756\" data-track-item_id=\"10.1364\/OPTICA.5.000756\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FOPTICA.5.000756\" aria-label=\"Article reference 38\" data-doi=\"10.1364\/OPTICA.5.000756\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2018Optic...5..756B\" aria-label=\"ADS reference 38\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 38\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Reinforcement%20learning%20in%20a%20large-scale%20photonic%20recurrent%20neural%20network&amp;journal=Optica&amp;doi=10.1364%2FOPTICA.5.000756&amp;volume=5&amp;pages=756-760&amp;publication_year=2018&amp;author=Bueno%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Kanno, K., Naruse, M. &amp; Uchida, A. Adaptive model selection in photonic reservoir computing by reinforcement learning. Sci. Rep. 10, 10062 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41598-020-66441-8\" data-track-item_id=\"10.1038\/s41598-020-66441-8\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41598-020-66441-8\" aria-label=\"Article reference 39\" data-doi=\"10.1038\/s41598-020-66441-8\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2020NatSR..1010062K\" aria-label=\"ADS reference 39\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXht1CqurrF\" aria-label=\"CAS reference 39\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32572093\" aria-label=\"PubMed reference 39\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7308406\" aria-label=\"PubMed Central reference 39\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 39\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Adaptive%20model%20selection%20in%20photonic%20reservoir%20computing%20by%20reinforcement%20learning&amp;journal=Sci.%20Rep.&amp;doi=10.1038%2Fs41598-020-66441-8&amp;volume=10&amp;publication_year=2020&amp;author=Kanno%2CK&amp;author=Naruse%2CM&amp;author=Uchida%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Hermans, M., Burm, M., Van Vaerenbergh, T., Dambre, J. &amp; Bienstman, P. Trainable hardware for dynamical computing using error backpropagation through physical media. Nat. Commun. 6, 6729 (2015).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/ncomms7729\" data-track-item_id=\"10.1038\/ncomms7729\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fncomms7729\" aria-label=\"Article reference 40\" data-doi=\"10.1038\/ncomms7729\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2015NatCo...6.6729H\" aria-label=\"ADS reference 40\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2MXosVCltbs%3D\" aria-label=\"CAS reference 40\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=25801303\" aria-label=\"PubMed reference 40\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 40\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Trainable%20hardware%20for%20dynamical%20computing%20using%20error%20backpropagation%20through%20physical%20media&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fncomms7729&amp;volume=6&amp;publication_year=2015&amp;author=Hermans%2CM&amp;author=Burm%2CM&amp;author=Vaerenbergh%2CT&amp;author=Dambre%2CJ&amp;author=Bienstman%2CP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2, 034092 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 41\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Neuromorphic%20computing%20using%20non-volatile%20memory&amp;journal=Adv.%20Phys.%20X&amp;volume=2&amp;publication_year=2017&amp;author=Burr%2CGW\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR42\">Pai, S. et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398\u2013404 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.ade8450\" data-track-item_id=\"10.1126\/science.ade8450\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.ade8450\" aria-label=\"Article reference 42\" data-doi=\"10.1126\/science.ade8450\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023Sci...380..398P\" aria-label=\"ADS reference 42\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXovFehtrs%3D\" aria-label=\"CAS reference 42\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37104594\" aria-label=\"PubMed reference 42\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 42\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Experimentally%20realized%20in%20situ%20backpropagation%20for%20deep%20learning%20in%20photonic%20neural%20networks&amp;journal=Science&amp;doi=10.1126%2Fscience.ade8450&amp;volume=380&amp;pages=398-404&amp;publication_year=2023&amp;author=Pai%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR43\">Morichetti, F. et al. Non-invasive on-chip light observation by contactless waveguide conductivity monitoring. IEEE J. Sel. Top. Quantum Electron. 20, 292\u2013301 (2014).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/JSTQE.2014.2300046\" data-track-item_id=\"10.1109\/JSTQE.2014.2300046\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FJSTQE.2014.2300046\" aria-label=\"Article reference 43\" data-doi=\"10.1109\/JSTQE.2014.2300046\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2014IJSTQ..20..292M\" aria-label=\"ADS reference 43\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 43\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Non-invasive%20on-chip%20light%20observation%20by%20contactless%20waveguide%20conductivity%20monitoring&amp;journal=IEEE%20J.%20Sel.%20Top.%20Quantum%20Electron.&amp;doi=10.1109%2FJSTQE.2014.2300046&amp;volume=20&amp;pages=292-301&amp;publication_year=2014&amp;author=Morichetti%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR44\">Zhou, T. et al. In situ optical backpropagation training of diffractive optical neural networks. Photonics Res. 8, 940\u2013953 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/PRJ.389553\" data-track-item_id=\"10.1364\/PRJ.389553\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FPRJ.389553\" aria-label=\"Article reference 44\" data-doi=\"10.1364\/PRJ.389553\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXitFGiurvM\" aria-label=\"CAS reference 44\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 44\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=In%20situ%20optical%20backpropagation%20training%20of%20diffractive%20optical%20neural%20networks&amp;journal=Photonics%20Res.&amp;doi=10.1364%2FPRJ.389553&amp;volume=8&amp;pages=940-953&amp;publication_year=2020&amp;author=Zhou%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR45\">Guo, X., Barrett, T. D., Wang, Z. M. &amp; Lvovsky, A. Backpropagation through nonlinear units for the all-optical training of neural networks. Photonics Res. 9, B71\u2013B80 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1364\/PRJ.411104\" data-track-item_id=\"10.1364\/PRJ.411104\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1364%2FPRJ.411104\" aria-label=\"Article reference 45\" data-doi=\"10.1364\/PRJ.411104\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 45\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Backpropagation%20through%20nonlinear%20units%20for%20the%20all-optical%20training%20of%20neural%20networks&amp;journal=Photonics%20Res.&amp;doi=10.1364%2FPRJ.411104&amp;volume=9&amp;pages=B71-B80&amp;publication_year=2021&amp;author=Guo%2CX&amp;author=Barrett%2CTD&amp;author=Wang%2CZM&amp;author=Lvovsky%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR46\">Wanjura, C. C. &amp; Marquardt, F. Fully nonlinear neuromorphic computing with linear wave scattering. Nat. Phys. 20, 1434\u20131440 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41567-024-02534-9\" data-track-item_id=\"10.1038\/s41567-024-02534-9\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41567-024-02534-9\" aria-label=\"Article reference 46\" data-doi=\"10.1038\/s41567-024-02534-9\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhsFGgtrfM\" aria-label=\"CAS reference 46\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 46\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fully%20nonlinear%20neuromorphic%20computing%20with%20linear%20wave%20scattering&amp;journal=Nat.%20Phys.&amp;doi=10.1038%2Fs41567-024-02534-9&amp;volume=20&amp;pages=1434-1440&amp;publication_year=2024&amp;author=Wanjura%2CCC&amp;author=Marquardt%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR47\">Yildirim, M., Dinc, N. U., Oguz, I., Psaltis, D. &amp; Moser, C. Nonlinear processing with linear optics. Nat. Photon. 18, 1076\u20131082 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41566-024-01494-z\" data-track-item_id=\"10.1038\/s41566-024-01494-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41566-024-01494-z\" aria-label=\"Article reference 47\" data-doi=\"10.1038\/s41566-024-01494-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhs1yktbjJ\" aria-label=\"CAS reference 47\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 47\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Nonlinear%20processing%20with%20linear%20optics&amp;journal=Nat.%20Photon.&amp;doi=10.1038%2Fs41566-024-01494-z&amp;volume=18&amp;pages=1076-1082&amp;publication_year=2024&amp;author=Yildirim%2CM&amp;author=Dinc%2CNU&amp;author=Oguz%2CI&amp;author=Psaltis%2CD&amp;author=Moser%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR48\">Xia, F. et al. Nonlinear optical encoding enabled by recurrent linear scattering. Nat. Photon. 18, 1067\u20131075 (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR49\">Scellier, B. &amp; Bengio, Y. Equilibrium propagation: bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. 11, 24 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.3389\/fncom.2017.00024\" data-track-item_id=\"10.3389\/fncom.2017.00024\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.3389%2Ffncom.2017.00024\" aria-label=\"Article reference 49\" data-doi=\"10.3389\/fncom.2017.00024\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=28522969\" aria-label=\"PubMed reference 49\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5415673\" aria-label=\"PubMed Central reference 49\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 49\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Equilibrium%20propagation%3A%20bridging%20the%20gap%20between%20energy-based%20models%20and%20backpropagation&amp;journal=Front.%20Comput.%20Neurosci.&amp;doi=10.3389%2Ffncom.2017.00024&amp;volume=11&amp;publication_year=2017&amp;author=Scellier%2CB&amp;author=Bengio%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR50\">Ackley, D. H., Hinton, G. E. &amp; Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147\u2013169 (1985).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 50\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20learning%20algorithm%20for%20Boltzmann%20machines&amp;journal=Cogn.%20Sci.&amp;volume=9&amp;pages=147-169&amp;publication_year=1985&amp;author=Ackley%2CDH&amp;author=Hinton%2CGE&amp;author=Sejnowski%2CTJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR51\">Stern, M., Hexner, D., Rocks, J. W. &amp; Liu, A. J. Supervised learning in physical networks: from machine learning to learning machines. Phys. Rev. X 11, 021045 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3MXhsFSgsrvF\" aria-label=\"CAS reference 51\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 51\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Supervised%20learning%20in%20physical%20networks%3A%20from%20machine%20learning%20to%20learning%20machines&amp;journal=Phys.%20Rev.%20X&amp;volume=11&amp;publication_year=2021&amp;author=Stern%2CM&amp;author=Hexner%2CD&amp;author=Rocks%2CJW&amp;author=Liu%2CAJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR52\">Scellier, B., Ernoult, M., Kendall, J. &amp; Kumar, S. Energy-based learning algorithms for analog computing: a comparative study. In Proc. 37th International Conference on Neural Information Processing Systems (NIPS \u201923), 52705\u201352731 (ACM, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR53\">Kendall, J., Pantone, R., Manickavasagam, K., Bengio, Y. &amp; Scellier, B. Training end-to-end analog neural networks with equilibrium propagation. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2006.01981\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2006.01981\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2006.01981<\/a> (2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR54\">Wang, Q., Wanjura, C. C. &amp; Marquardt, F. Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation. Neuromorph. Comput. Eng. 4, 034014 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1088\/2634-4386\/ad752b\" data-track-item_id=\"10.1088\/2634-4386\/ad752b\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1088%2F2634-4386%2Fad752b\" aria-label=\"Article reference 54\" data-doi=\"10.1088\/2634-4386\/ad752b\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 54\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Training%20coupled%20phase%20oscillators%20as%20a%20neuromorphic%20platform%20using%20equilibrium%20propagation&amp;journal=Neuromorph.%20Comput.%20Eng.&amp;doi=10.1088%2F2634-4386%2Fad752b&amp;volume=4&amp;publication_year=2024&amp;author=Wang%2CQ&amp;author=Wanjura%2CCC&amp;author=Marquardt%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR55\">Yi, S.-i, Kendall, J. D., Williams, R. S. &amp; Kumar, S. Activity-difference training of deep neural networks using memristor crossbars. Nat. Electron. 6, 45\u201351 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 55\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Activity-difference%20training%20of%20deep%20neural%20networks%20using%20memristor%20crossbars&amp;journal=Nat.%20Electron.&amp;volume=6&amp;pages=45-51&amp;publication_year=2023&amp;author=Yi%2CS-i&amp;author=Kendall%2CJD&amp;author=Williams%2CRS&amp;author=Kumar%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR56\">Laydevant, J., Markovi\u0107, D. &amp; Grollier, J. Training an Ising machine with equilibrium propagation. Nat. Commun. 15, 3671 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-024-46879-4\" data-track-item_id=\"10.1038\/s41467-024-46879-4\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-024-46879-4\" aria-label=\"Article reference 56\" data-doi=\"10.1038\/s41467-024-46879-4\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024NatCo..15.3671L\" aria-label=\"ADS reference 56\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXpvFKksrs%3D\" aria-label=\"CAS reference 56\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38693108\" aria-label=\"PubMed reference 56\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11063034\" aria-label=\"PubMed Central reference 56\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 56\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Training%20an%20Ising%20machine%20with%20equilibrium%20propagation&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-024-46879-4&amp;volume=15&amp;publication_year=2024&amp;author=Laydevant%2CJ&amp;author=Markovi%C4%87%2CD&amp;author=Grollier%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR57\">Altman, L. E., Stern, M., Liu, A. J. &amp; Durian, D. J. Experimental demonstration of coupled learning in elastic networks. Phys. Rev. Appl. 22, 024053 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1103\/PhysRevApplied.22.024053\" data-track-item_id=\"10.1103\/PhysRevApplied.22.024053\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1103%2FPhysRevApplied.22.024053\" aria-label=\"Article reference 57\" data-doi=\"10.1103\/PhysRevApplied.22.024053\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXit1yhurjP\" aria-label=\"CAS reference 57\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 57\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Experimental%20demonstration%20of%20coupled%20learning%20in%20elastic%20networks&amp;journal=Phys.%20Rev.%20Appl.&amp;doi=10.1103%2FPhysRevApplied.22.024053&amp;volume=22&amp;publication_year=2024&amp;author=Altman%2CLE&amp;author=Stern%2CM&amp;author=Liu%2CAJ&amp;author=Durian%2CDJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR58\">Dillavou, S., Stern, M., Liu, A. J. &amp; Durian, D. J. Demonstration of decentralized physics-driven learning. Phys. Rev. Appl. 18, 014040 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1103\/PhysRevApplied.18.014040\" data-track-item_id=\"10.1103\/PhysRevApplied.18.014040\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1103%2FPhysRevApplied.18.014040\" aria-label=\"Article reference 58\" data-doi=\"10.1103\/PhysRevApplied.18.014040\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022PhRvP..18a4040D\" aria-label=\"ADS reference 58\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XitFSgs7%2FP\" aria-label=\"CAS reference 58\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 58\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Demonstration%20of%20decentralized%20physics-driven%20learning&amp;journal=Phys.%20Rev.%20Appl.&amp;doi=10.1103%2FPhysRevApplied.18.014040&amp;volume=18&amp;publication_year=2022&amp;author=Dillavou%2CS&amp;author=Stern%2CM&amp;author=Liu%2CAJ&amp;author=Durian%2CDJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR59\">Dillavou, S. et al. Machine learning without a processor: emergent learning in a nonlinear analog network. Proc. Natl Acad. Sci. 121, e2319718121 (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR60\">Stern, M., Dillavou, S., Jayaraman, D., Duria, D. J. &amp; Liu, A. J. Training self-learning circuits for power-efficient solutions. APL Mach. Learn. 2, 016114 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1063\/5.0181382\" data-track-item_id=\"10.1063\/5.0181382\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1063%2F5.0181382\" aria-label=\"Article reference 60\" data-doi=\"10.1063\/5.0181382\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 60\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Training%20self-learning%20circuits%20for%20power-efficient%20solutions&amp;journal=APL%20Mach.%20Learn.&amp;doi=10.1063%2F5.0181382&amp;volume=2&amp;publication_year=2024&amp;author=Stern%2CM&amp;author=Dillavou%2CS&amp;author=Jayaraman%2CD&amp;author=Duria%2CDJ&amp;author=Liu%2CAJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR61\">Anisetti, V. R., Kandala, A., Scellier, B. &amp; Schwarz, J. Frequency propagation: multimechanism learning in nonlinear physical networks. Neural Comput. 36, 596\u2013620 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1162\/neco_a_01648\" data-track-item_id=\"10.1162\/neco_a_01648\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1162%2Fneco_a_01648\" aria-label=\"Article reference 61\" data-doi=\"10.1162\/neco_a_01648\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=4725530\" aria-label=\"MathSciNet reference 61\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38457749\" aria-label=\"PubMed reference 61\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 61\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Frequency%20propagation%3A%20multimechanism%20learning%20in%20nonlinear%20physical%20networks&amp;journal=Neural%20Comput.&amp;doi=10.1162%2Fneco_a_01648&amp;volume=36&amp;pages=596-620&amp;publication_year=2024&amp;author=Anisetti%2CVR&amp;author=Kandala%2CA&amp;author=Scellier%2CB&amp;author=Schwarz%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR62\">Murugan, A., Strupp, A., Scellier, B. &amp; Falk, M. Contrastive learning through non-equilibrium memory. In APS March Meeting Abstracts 2023, F02.005 (APS, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR63\">Laborieux, A. &amp; Zenke, F. Holomorphic equilibrium propagation computes exact gradients through finite size oscillations. In Proc. 36th International Conference on Neural Information Processing Systems (NIPS \u201922), 12950\u201312963 (ACM, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR64\">Scellier, B., Mishra, S., Bengio, Y. &amp; Ollivier, Y. Agnostic physics-driven deep learning. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2205.15021\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2205.15021\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2205.15021<\/a> (2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR65\">Lopez-Pastor, V. &amp; Marquardt, F. Self-learning machines based on Hamiltonian echo backpropagation. Phys. Rev. X 13, 031020 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXitFOhsr7K\" aria-label=\"CAS reference 65\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 65\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Self-learning%20machines%20based%20on%20Hamiltonian%20echo%20backpropagation&amp;journal=Phys.%20Rev.%20X&amp;volume=13&amp;publication_year=2023&amp;author=Lopez-Pastor%2CV&amp;author=Marquardt%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR66\">Touvron, H. et al. LLaMA: open and efficient foundation language models. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2302.13971\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2302.13971\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2302.13971<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR67\">Chowdhery, A. et al. PaLM: scaling language modeling with pathways. J. Mach. Learn. Res. 24, 1\u2013113 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 67\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=PaLM%3A%20scaling%20language%20modeling%20with%20pathways&amp;journal=J.%20Mach.%20Learn.%20Res.&amp;volume=24&amp;pages=1-113&amp;publication_year=2023&amp;author=Chowdhery%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR68\">Achiam, J. et al. GPT-4 technical report. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2303.08774v1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2303.08774v1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2303.08774v1<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR69\">Team, G. Gemini: a family of highly capable multimodal models. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2312.11805v1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2312.11805v1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2312.11805v1<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR70\">Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning, 8748\u20138763 (MLR Press, 2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR71\">Liu, H., Li, C., Wu, Q. &amp; Lee, Y. J. Visual instruction tuning. In Proc. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) <a href=\"https:\/\/openreview.net\/forum?id=w0H2xGHlkw\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/openreview.net\/forum?id=w0H2xGHlkw\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/openreview.net\/forum?id=w0H2xGHlkw<\/a> (OpenReview, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR72\">Radford, A. et al. Language models are unsupervised multitask learners. OpenAI Blog 1, 9 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 72\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Language%20models%20are%20unsupervised%20multitask%20learners&amp;journal=OpenAI%20Blog&amp;volume=1&amp;publication_year=2019&amp;author=Radford%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR73\">Katharopoulos, A., Vyas, A., Pappas, N. &amp; Fleuret, F. Transformers are RNNs: fast autoregressive transformers with linear attention. In Proc. 37th International Conference on Machine Learning, 5156\u20135165 (MLR Press, 2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR74\">Gu, A. &amp; Dao, T. Mamba: linear-time sequence modeling with selective state spaces. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2312.00752v1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2312.00752v1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2312.00752v1<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR75\">Wang, H. et al. BitNet: scaling 1-bit transformers for large language models. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2310.11453\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2310.11453\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2310.11453<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR76\">Hu, E. J. et al. LoRA: low-rank adaptation of large language models. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2106.09685\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2106.09685\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2106.09685<\/a> (2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR77\">Dao, T., Fu, D., Ermon, S., Rudra, A. &amp; R\u00e9, C. FLASHATTENTION: fast and memory-efficient exact attention with IO-awareness. In Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) 35, 16344\u201316359 (ACM, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR78\">Juravsky, J. et al. Hydragen: high-throughput LLM inference with shared prefixes. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2402.05099\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2402.05099\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2402.05099<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR79\">Anderson, M. G., Ma, S.-Y., Wang, T., Wright, L. G. &amp; McMahon, P. L. Optical transformers. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2302.10360\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2302.10360\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2302.10360<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR80\">Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441\u2013446 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nphoton.2017.93\" data-track-item_id=\"10.1038\/nphoton.2017.93\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnphoton.2017.93\" aria-label=\"Article reference 80\" data-doi=\"10.1038\/nphoton.2017.93\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2017NaPho..11..441S\" aria-label=\"ADS reference 80\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2sXhtVSjt7bJ\" aria-label=\"CAS reference 80\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 80\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20learning%20with%20coherent%20nanophotonic%20circuits&amp;journal=Nat.%20Photon.&amp;doi=10.1038%2Fnphoton.2017.93&amp;volume=11&amp;pages=441-446&amp;publication_year=2017&amp;author=Shen%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR81\">Hamerly, R., Bernstein, L., Sludds, A., Solja\u010di\u0107, M. &amp; Englund, D. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9, 021032 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC1MXhsFOrurnI\" aria-label=\"CAS reference 81\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 81\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Large-scale%20optical%20neural%20networks%20based%20on%20photoelectric%20multiplication&amp;journal=Phys.%20Rev.%20X&amp;volume=9&amp;publication_year=2019&amp;author=Hamerly%2CR&amp;author=Bernstein%2CL&amp;author=Sludds%2CA&amp;author=Solja%C4%8Di%C4%87%2CM&amp;author=Englund%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR82\">Tait, A. N. Quantifying power in silicon photonic neural networks. Phys. Rev. Appl. 17, 054029 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1103\/PhysRevApplied.17.054029\" data-track-item_id=\"10.1103\/PhysRevApplied.17.054029\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1103%2FPhysRevApplied.17.054029\" aria-label=\"Article reference 82\" data-doi=\"10.1103\/PhysRevApplied.17.054029\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022PhRvP..17e4029T\" aria-label=\"ADS reference 82\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 82\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Quantifying%20power%20in%20silicon%20photonic%20neural%20networks&amp;journal=Phys.%20Rev.%20Appl.&amp;doi=10.1103%2FPhysRevApplied.17.054029&amp;volume=17&amp;publication_year=2022&amp;author=Tait%2CAN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR83\">Laydevant, J., Wright, L. G., Wang, T. &amp; McMahon, P. L. The hardware is the software. Neuron 112, 180\u2013183 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.neuron.2023.11.004\" data-track-item_id=\"10.1016\/j.neuron.2023.11.004\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.neuron.2023.11.004\" aria-label=\"Article reference 83\" data-doi=\"10.1016\/j.neuron.2023.11.004\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXisFyjs7fO\" aria-label=\"CAS reference 83\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38086371\" aria-label=\"PubMed reference 83\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 83\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20hardware%20is%20the%20software&amp;journal=Neuron&amp;doi=10.1016%2Fj.neuron.2023.11.004&amp;volume=112&amp;pages=180-183&amp;publication_year=2024&amp;author=Laydevant%2CJ&amp;author=Wright%2CLG&amp;author=Wang%2CT&amp;author=McMahon%2CPL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR84\">Hooker, S. The hardware lottery. Commun. ACM 64, 58\u201365 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1145\/3467017\" data-track-item_id=\"10.1145\/3467017\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1145%2F3467017\" aria-label=\"Article reference 84\" data-doi=\"10.1145\/3467017\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 84\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20hardware%20lottery&amp;journal=Commun.%20ACM&amp;doi=10.1145%2F3467017&amp;volume=64&amp;pages=58-65&amp;publication_year=2021&amp;author=Hooker%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR85\">Stroev, N. &amp; Berloff, N. G. Analog photonics computing for information processing, inference, and optimization. Adv. Quantum Technol. 6, 2300055 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1002\/qute.202300055\" data-track-item_id=\"10.1002\/qute.202300055\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1002%2Fqute.202300055\" aria-label=\"Article reference 85\" data-doi=\"10.1002\/qute.202300055\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 85\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Analog%20photonics%20computing%20for%20information%20processing%2C%20inference%2C%20and%20optimization&amp;journal=Adv.%20Quantum%20Technol.&amp;doi=10.1002%2Fqute.202300055&amp;volume=6&amp;publication_year=2023&amp;author=Stroev%2CN&amp;author=Berloff%2CNG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR86\">Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L. &amp; Coles, P. J. Challenges and opportunities in quantum machine learning. Nat. Comput. Sci. 2, 567\u2013576 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s43588-022-00311-3\" data-track-item_id=\"10.1038\/s43588-022-00311-3\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs43588-022-00311-3\" aria-label=\"Article reference 86\" data-doi=\"10.1038\/s43588-022-00311-3\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:STN:280:DC%2BB1c3itlaqtg%3D%3D\" aria-label=\"CAS reference 86\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38177473\" aria-label=\"PubMed reference 86\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 86\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Challenges%20and%20opportunities%20in%20quantum%20machine%20learning&amp;journal=Nat.%20Comput.%20Sci.&amp;doi=10.1038%2Fs43588-022-00311-3&amp;volume=2&amp;pages=567-576&amp;publication_year=2022&amp;author=Cerezo%2CM&amp;author=Verdon%2CG&amp;author=Huang%2CH-Y&amp;author=Cincio%2CL&amp;author=Coles%2CPJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR87\">Kashif, M. &amp; Shafique, M. Hqnet: harnessing quantum noise for effective training of quantum neural networks in NISQ era. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/2402.08475v1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/2402.08475v1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2402.08475v1<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR88\">Zhou, M.-G. et al. Quantum neural network for quantum neural computing. Research 6, 0134 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.34133\/research.0134\" data-track-item_id=\"10.34133\/research.0134\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.34133%2Fresearch.0134\" aria-label=\"Article reference 88\" data-doi=\"10.34133\/research.0134\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023Resea...6...94Z\" aria-label=\"ADS reference 88\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37223480\" aria-label=\"PubMed reference 88\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10202373\" aria-label=\"PubMed Central reference 88\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 88\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Quantum%20neural%20network%20for%20quantum%20neural%20computing&amp;journal=Research&amp;doi=10.34133%2Fresearch.0134&amp;volume=6&amp;publication_year=2023&amp;author=Zhou%2CM-G\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR89\">Tian, J. et al. Recent advances for quantum neural networks in generative learning. IEEE Trans. Pattern. Anal. Mach. Intell. 45, 12321\u201312340 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TPAMI.2023.3272029\" data-track-item_id=\"10.1109\/TPAMI.2023.3272029\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTPAMI.2023.3272029\" aria-label=\"Article reference 89\" data-doi=\"10.1109\/TPAMI.2023.3272029\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37126624\" aria-label=\"PubMed reference 89\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 89\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Recent%20advances%20for%20quantum%20neural%20networks%20in%20generative%20learning&amp;journal=IEEE%20Trans.%20Pattern.%20Anal.%20Mach.%20Intell.&amp;doi=10.1109%2FTPAMI.2023.3272029&amp;volume=45&amp;pages=12321-12340&amp;publication_year=2023&amp;author=Tian%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR90\">Cerezo, M. et al. Variational quantum algorithms. Nat. Rev. Phys. 3, 625\u2013644 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s42254-021-00348-9\" data-track-item_id=\"10.1038\/s42254-021-00348-9\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs42254-021-00348-9\" aria-label=\"Article reference 90\" data-doi=\"10.1038\/s42254-021-00348-9\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 90\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Variational%20quantum%20algorithms&amp;journal=Nat.%20Rev.%20Phys.&amp;doi=10.1038%2Fs42254-021-00348-9&amp;volume=3&amp;pages=625-644&amp;publication_year=2021&amp;author=Cerezo%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR91\">Niazi, S. et al. Training deep Boltzmann networks with sparse Ising machines. Nat. Electron. 7, 610\u2013619 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41928-024-01182-4\" data-track-item_id=\"10.1038\/s41928-024-01182-4\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41928-024-01182-4\" aria-label=\"Article reference 91\" data-doi=\"10.1038\/s41928-024-01182-4\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 91\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Training%20deep%20Boltzmann%20networks%20with%20sparse%20Ising%20machines&amp;journal=Nat.%20Electron.&amp;doi=10.1038%2Fs41928-024-01182-4&amp;volume=7&amp;pages=610-619&amp;publication_year=2024&amp;author=Niazi%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR92\">Ma, S. Y., Wang, T., Laydevant, J., Wright, L. G. &amp; McMahon, P. L. Quantum-limited stochastic optical neural networks operating at a few quanta per activation. Nat. Commun. 16, 359 (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR93\">Pierangeli, D., Marcucci, G., Brunner, D. &amp; Conti, C. Noise-enhanced spatial-photonic Ising machine. Nanophotonics 9, 4109\u20134116 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1515\/nanoph-2020-0119\" data-track-item_id=\"10.1515\/nanoph-2020-0119\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1515%2Fnanoph-2020-0119\" aria-label=\"Article reference 93\" data-doi=\"10.1515\/nanoph-2020-0119\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 93\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Noise-enhanced%20spatial-photonic%20Ising%20machine&amp;journal=Nanophotonics&amp;doi=10.1515%2Fnanoph-2020-0119&amp;volume=9&amp;pages=4109-4116&amp;publication_year=2020&amp;author=Pierangeli%2CD&amp;author=Marcucci%2CG&amp;author=Brunner%2CD&amp;author=Conti%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR94\">McMahon, P. L. The physics of optical computing. Nat. Rev. Phys. 5, 717\u2013734 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s42254-023-00645-5\" data-track-item_id=\"10.1038\/s42254-023-00645-5\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs42254-023-00645-5\" aria-label=\"Article reference 94\" data-doi=\"10.1038\/s42254-023-00645-5\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 94\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20physics%20of%20optical%20computing&amp;journal=Nat.%20Rev.%20Phys.&amp;doi=10.1038%2Fs42254-023-00645-5&amp;volume=5&amp;pages=717-734&amp;publication_year=2023&amp;author=McMahon%2CPL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR95\">Keeling, J. &amp; Berloff, N. G. Exciton\u2013polariton condensation. Contemp. Phys. 52, 131\u2013151 (2011).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1080\/00107514.2010.550120\" data-track-item_id=\"10.1080\/00107514.2010.550120\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1080%2F00107514.2010.550120\" aria-label=\"Article reference 95\" data-doi=\"10.1080\/00107514.2010.550120\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2011ConPh..52..131K\" aria-label=\"ADS reference 95\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3MXhvFKjt70%3D\" aria-label=\"CAS reference 95\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 95\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Exciton%E2%80%93polariton%20condensation&amp;journal=Contemp.%20Phys.&amp;doi=10.1080%2F00107514.2010.550120&amp;volume=52&amp;pages=131-151&amp;publication_year=2011&amp;author=Keeling%2CJ&amp;author=Berloff%2CNG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR96\">Berloff, N. G. et al. Realizing the classical XY Hamiltonian in polariton simulators. Nat. Mater. 16, 1120\u20131126 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nmat4971\" data-track-item_id=\"10.1038\/nmat4971\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnmat4971\" aria-label=\"Article reference 96\" data-doi=\"10.1038\/nmat4971\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2017NatMa..16.1120B\" aria-label=\"ADS reference 96\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2sXhsFOju7fM\" aria-label=\"CAS reference 96\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=28967915\" aria-label=\"PubMed reference 96\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 96\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Realizing%20the%20classical%20XY%20Hamiltonian%20in%20polariton%20simulators&amp;journal=Nat.%20Mater.&amp;doi=10.1038%2Fnmat4971&amp;volume=16&amp;pages=1120-1126&amp;publication_year=2017&amp;author=Berloff%2CNG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR97\">Johnston, A. &amp; Berloff, N. G. Macroscopic noise amplification by asymmetric dyads in non-Hermitian optical systems for generative diffusion models. Phys. Rev. Lett. 132, 096901 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1103\/PhysRevLett.132.096901\" data-track-item_id=\"10.1103\/PhysRevLett.132.096901\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1103%2FPhysRevLett.132.096901\" aria-label=\"Article reference 97\" data-doi=\"10.1103\/PhysRevLett.132.096901\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024PhRvL.132i6901J\" aria-label=\"ADS reference 97\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=4721433\" aria-label=\"MathSciNet reference 97\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXlvVOjs7Y%3D\" aria-label=\"CAS reference 97\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38489613\" aria-label=\"PubMed reference 97\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 97\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Macroscopic%20noise%20amplification%20by%20asymmetric%20dyads%20in%20non-Hermitian%20optical%20systems%20for%20generative%20diffusion%20models&amp;journal=Phys.%20Rev.%20Lett.&amp;doi=10.1103%2FPhysRevLett.132.096901&amp;volume=132&amp;publication_year=2024&amp;author=Johnston%2CA&amp;author=Berloff%2CNG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR98\">Wang, T. et al. Image sensing with multilayer nonlinear optical neural networks. Nat. Photon. 17, 408\u2013415 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41566-023-01170-8\" data-track-item_id=\"10.1038\/s41566-023-01170-8\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41566-023-01170-8\" aria-label=\"Article reference 98\" data-doi=\"10.1038\/s41566-023-01170-8\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NaPho..17..408W\" aria-label=\"ADS reference 98\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXmtFWhs78%3D\" aria-label=\"CAS reference 98\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 98\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Image%20sensing%20with%20multilayer%20nonlinear%20optical%20neural%20networks&amp;journal=Nat.%20Photon.&amp;doi=10.1038%2Fs41566-023-01170-8&amp;volume=17&amp;pages=408-415&amp;publication_year=2023&amp;author=Wang%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR99\">Zhou, F. &amp; Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 3, 664\u2013671 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41928-020-00501-9\" data-track-item_id=\"10.1038\/s41928-020-00501-9\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41928-020-00501-9\" aria-label=\"Article reference 99\" data-doi=\"10.1038\/s41928-020-00501-9\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 99\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Near-sensor%20and%20in-sensor%20computing&amp;journal=Nat.%20Electron.&amp;doi=10.1038%2Fs41928-020-00501-9&amp;volume=3&amp;pages=664-671&amp;publication_year=2020&amp;author=Zhou%2CF&amp;author=Chai%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR100\">del Hougne, P., F. Imani, M., Diebold, A. V., Horstmeyer, R. &amp; Smith, D. R. Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network. Adv. Sci. 7, 1901913 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1002\/advs.201901913\" data-track-item_id=\"10.1002\/advs.201901913\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1002%2Fadvs.201901913\" aria-label=\"Article reference 100\" data-doi=\"10.1002\/advs.201901913\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 100\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Learned%20integrated%20sensing%20pipeline%3A%20reconfigurable%20metasurface%20transceivers%20as%20trainable%20physical%20layer%20in%20an%20artificial%20neural%20network&amp;journal=Adv.%20Sci.&amp;doi=10.1002%2Fadvs.201901913&amp;volume=7&amp;publication_year=2020&amp;author=Hougne%2CP&amp;author=F.%20Imani%2CM&amp;author=Diebold%2CAV&amp;author=Horstmeyer%2CR&amp;author=Smith%2CDR\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR101\">Vaswani, A. et al. Attention is all you need. In Proc. 31st International Conference on Neural Information Processing Systems (NIPS \u201917), 6000\u20136010 (ACM, 2017).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR102\">Wu, C. et al. Harnessing optoelectronic noises in a photonic generative network. Sci. Adv. 8, eabm2956 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/sciadv.abm2956\" data-track-item_id=\"10.1126\/sciadv.abm2956\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fsciadv.abm2956\" aria-label=\"Article reference 102\" data-doi=\"10.1126\/sciadv.abm2956\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2022SciA....8.2956W\" aria-label=\"ADS reference 102\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XjtlOmurw%3D\" aria-label=\"CAS reference 102\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35061531\" aria-label=\"PubMed reference 102\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8782447\" aria-label=\"PubMed Central reference 102\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 102\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Harnessing%20optoelectronic%20noises%20in%20a%20photonic%20generative%20network&amp;journal=Sci.%20Adv.&amp;doi=10.1126%2Fsciadv.abm2956&amp;volume=8&amp;publication_year=2022&amp;author=Wu%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR103\">Bonnet, D. et al. Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. Nat. Commun. 14, 7530 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-023-43317-9\" data-track-item_id=\"10.1038\/s41467-023-43317-9\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-023-43317-9\" aria-label=\"Article reference 103\" data-doi=\"10.1038\/s41467-023-43317-9\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023NatCo..14.7530B\" aria-label=\"ADS reference 103\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXisVektb%2FK\" aria-label=\"CAS reference 103\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37985669\" aria-label=\"PubMed reference 103\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10661910\" aria-label=\"PubMed Central reference 103\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 103\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Bringing%20uncertainty%20quantification%20to%20the%20extreme-edge%20with%20memristor-based%20Bayesian%20neural%20networks&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-023-43317-9&amp;volume=14&amp;publication_year=2023&amp;author=Bonnet%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR104\">Olin-Ammentorp, W., Beckmann, K., Schuman, C. D., Plank, J. S. &amp; Cady, N. C. Stochasticity and robustness in spiking neural networks. Neurocomputing 419, 23\u201336 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.neucom.2020.07.105\" data-track-item_id=\"10.1016\/j.neucom.2020.07.105\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.neucom.2020.07.105\" aria-label=\"Article reference 104\" data-doi=\"10.1016\/j.neucom.2020.07.105\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 104\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Stochasticity%20and%20robustness%20in%20spiking%20neural%20networks&amp;journal=Neurocomputing&amp;doi=10.1016%2Fj.neucom.2020.07.105&amp;volume=419&amp;pages=23-36&amp;publication_year=2021&amp;author=Olin-Ammentorp%2CW&amp;author=Beckmann%2CK&amp;author=Schuman%2CCD&amp;author=Plank%2CJS&amp;author=Cady%2CNC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n","protected":false},"excerpt":{"rendered":"Samborska, V. Scaling up: how increasing inputs has made artificial intelligence more capable. Our World in Data https:\/\/ourworldindata.org\/scaling-up-ai&hellip;\n","protected":false},"author":2,"featured_media":114149,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[554,733,4308,35334,4230,11959,4231,55000,90,86,56,54,55],"class_list":["post-114148","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-electronics","tag-humanities-and-social-sciences","tag-mathematics-and-computing","tag-multidisciplinary","tag-photonics-and-device-physics","tag-science","tag-technology","tag-uk","tag-united-kingdom","tag-unitedkingdom"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/114148","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=114148"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/114148\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/114149"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=114148"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=114148"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=114148"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}