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The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL (Yemini et al., 2021). Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group (Chaudhary et al., 2021). Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.\n                  <\/jats:p>","DOI":"10.7554\/elife.66410","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T08:00:27Z","timestamp":1626249627000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.66410","source":"Crossref","is-referenced-by-count":48,"title":["Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8699-3546","authenticated-orcid":true,"given":"Xinwei","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Physics, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9458-0629","authenticated-orcid":true,"given":"Matthew S","family":"Creamer","sequence":"additional","affiliation":[{"name":"Princeton Neuroscience Institute, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6200-7254","authenticated-orcid":true,"given":"Francesco","family":"Randi","sequence":"additional","affiliation":[{"name":"Department of Physics, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5061-9731","authenticated-orcid":true,"given":"Anuj K","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Physics, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3878-9073","authenticated-orcid":true,"given":"Scott W","family":"Linderman","sequence":"additional","affiliation":[{"name":"Department of Statistics, Stanford University, Stanford, United States"},{"name":"Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5362-5093","authenticated-orcid":true,"given":"Andrew M","family":"Leifer","sequence":"additional","affiliation":[{"name":"Department of Physics, Princeton University, Princeton, United States"},{"name":"Princeton Neuroscience Institute, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4374","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"bib1","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"key":"bib2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1995.466933","article-title":"Rock, paper, and scissors: extrinsic vs. intrinsic similarity of non-rigid shapes","author":"Bronstein","year":"2007","unstructured":"Bronstein AM. 2007. 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