{"doi":"10.7554/elife.66410","title":"Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training","abstract":", called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. 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.","journal":"eLife","year":2021,"id":162539,"datarank":1.5108050225440288,"base_score":3.8501476017100584,"endowment":3.8501476017100584,"self_citation_contribution":0.5775221402565088,"citation_network_contribution":0.9332828822875201,"self_endowment_contribution":0.5775221402565088,"citer_contribution":0.9332828822875201,"corpus_percentile":null,"corpus_rank":null,"citation_count":46,"citer_count":42,"citers_with_citation_signal":30,"citers_with_endowment":30,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.948,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":679936,"name":"Matthew S. Creamer","orcid":"0000-0002-9458-0629","position":1,"is_corresponding":false},{"id":634529,"name":"Francesco Randi","orcid":"0000-0002-6200-7254","position":2,"is_corresponding":false},{"id":634530,"name":"Anuj Kumar Sharma","orcid":"0000-0001-5061-9731","position":3,"is_corresponding":false},{"id":539625,"name":"Scott W. Linderman","orcid":"0000-0002-3878-9073","position":4,"is_corresponding":false},{"id":634531,"name":"Andrew M. Leifer","orcid":"0000-0002-5362-5093","position":5,"is_corresponding":false},{"id":634528,"name":"Xinwei Yu","orcid":"0000-0002-8699-3546","position":0,"is_corresponding":true}],"reference_count":59,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:45:05.641178Z","pmid":"34259623","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}