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Neural Circuits"],"abstract":"<jats:p>\n                    The hermaphroditic\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    , with its fully mapped connectome of 302 neurons, offers a paradigmatic example of how a minimal nervous system governs biotic, adaptive, and context-dependent behaviors. In contrast, modern artificial intelligence systems achieve intelligence through scale rather than efficiency, relying instead on massive datasets and artificially engineered architectures. This mini-review explores how\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    neural circuits can inform the development of more efficient and flexible artificial neural networks. We highlight recent studies that translate the principles inherent to\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    neural circuits into artificial neural network architectures, with applications in machine control and image classification, resulting in enhanced robustness and improved performance. By distilling neural principles from the simplest known nervous system, this mini-review outlines a pathway toward compact, adaptive, and biologically inspired artificial intelligence systems.\n                  <\/jats:p>","DOI":"10.3389\/fncir.2026.1731513","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:47:38Z","timestamp":1773038858000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From small brains to smart machines: translating Caenorhabditis elegans neural circuits into artificial intelligence"],"prefix":"10.3389","volume":"20","author":[{"given":"He","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Guangdong","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panpan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Guangdong","place":["Zhuhai, China"]},{"name":"School of Systems Science, Beijing Normal University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuebin","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Intelligence Science and Technology, Guangdong","place":["Zhuhai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/bs.mie.2014.10.025","article-title":"RNA-Seq profiling of small numbers of drosophila neurons","volume":"551","author":"Abruzzi","year":"2015","journal-title":"Methods Enzymol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1148\/radiol.10081634","article-title":"Principles of and advances in percutaneous ablation","volume":"258","author":"Ahmed","year":"2011","journal-title":"Radiology"},{"key":"B3","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1126\/science.1173299","article-title":"Scale-free networks: a decade and beyond","volume":"325","author":"Barab\u00e1si","year":"2009","journal-title":"scienceScience"},{"key":"B4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-031-89704-7_9","article-title":"\u201cFP-elegans M1: feature pyramid reservoir connectome transformers and multi-backbone feature extractors for MEDMNIST2D-V2,\u201d","volume-title":"Computational Intelligence Methods for Bioinformatics and Biostatistics: 19th International Meeting, CIBB 2024","author":"Bardozzo","year":"2025"},{"key":"B5","doi-asserted-by":"publisher","first-page":"127598","DOI":"10.1016\/j.neucom.2024.127598","article-title":"Elegans-AI: how the connectome of a living organism could model artificial neural networks","volume":"584","author":"Bardozzo","year":"2024","journal-title":"Neurocomputing"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1895\/wormbook.1.123.1","volume":"2006","author":"Bargmann","year":"2006"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/9781601982957","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Foundat. 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