{"id":205381,"date":"2025-02-03T18:27:51","date_gmt":"2025-02-04T00:27:51","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/02\/organoid-intelligence-training-lab-grown-mini-brains-to-learn-and-compute-with-ai"},"modified":"2025-02-03T18:27:51","modified_gmt":"2025-02-04T00:27:51","slug":"organoid-intelligence-training-lab-grown-mini-brains-to-learn-and-compute-with-ai","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/02\/organoid-intelligence-training-lab-grown-mini-brains-to-learn-and-compute-with-ai","title":{"rendered":"Organoid intelligence: training lab-grown mini-brains to learn and compute with AI"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/organoid-intelligence-training-lab-grown-mini-brains-to-learn-and-compute-with-ai.jpg\"><\/a><\/p>\n<p>Recent research demonstrates that brain organoids can indeed \u201clearn\u201d and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also control the feedback signals, creating a biofeedback loop that allows the organoids to adapt and even demonstrate short-term memory (Bai et al. <a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2024\" title=\"Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J (2024) AI-enabled organoids: construction, analysis, and application. Bioact Mater 31:525&ndash;548. https:\/\/doi.org\/10.1016\/j.bioactmat.2023.09.005 \" href=\"https:\/\/aapsopen.springeropen.com\/articles\/10.1186\/s41120-025-00109-3#ref-CR2\" id=\"ref-link-section-d32734496e304\">2024<\/a>).<\/p>\n<p>One technique central to AI-integrated organoid computing is <i>reservoir computing<\/i>, a model traditionally used in silicon-based computing. In an open-loop setup, AI algorithms interact with organoids as they serve as the \u201creservoir,\u201d for processing input signals and dynamically adjusting their responses. By interpreting these responses, researchers can classify, predict, and understand how organoids adapt to specific inputs, suggesting the potential for simple computational processing within a biological substrate (Kagan et al. <a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2023\" title=\"Kagan BJ, Gyngell C, Lysaght T, Cole VM, Sawai T, Savulescu J (2023) The technology, opportunities, and challenges of synthetic biological intelligence. Biotechnol Adv 68:108233. https:\/\/doi.org\/10.1016\/j.biotechadv.2023.108233 \" href=\"https:\/\/aapsopen.springeropen.com\/articles\/10.1186\/s41120-025-00109-3#ref-CR5\" id=\"ref-link-section-d32734496e313\">2023<\/a>; Aaser et al. <a data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference n.d.\" title=\"Aaser P, Knudsen M, Ramstad OH, van de Wijdeven R, Nichele S, Sandvig I, Tufte G, Stefan Bauer U, Halaas \u00d8, Hendseth S, Sandvig A, Valderhaug V (2017) Towards making a cyborg: a closed-loop reservoir-neuro system. Proceedings of the ECAL 2017, the Fourteenth European Conference on Artificial Life. Lyon, ASME, pp 430&ndash;437. https:\/\/doi-org.umiss.idm.oclc.org\/10.1162\/isal_a_072 \" href=\"https:\/\/aapsopen.springeropen.com\/articles\/10.1186\/s41120-025-00109-3#ref-CR1\" id=\"ref-link-section-d32734496e316\">n.d.<\/a>).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent research demonstrates that brain organoids can indeed \u201clearn\u201d and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also [\u2026]<\/p>\n","protected":false},"author":661,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,41,47,6],"tags":[],"class_list":["post-205381","post","type-post","status-publish","format-standard","hentry","category-biological","category-information-science","category-neuroscience","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/205381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/661"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=205381"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/205381\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=205381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=205381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=205381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}