@article{afafc3e5e2ff44a9833477757dc8a928,
title = "Wfast deep neural correspondence for tracking and identifying neurons in c. Elegans using semi-synthetic training",
abstract = "We present an automated method to track and identify neurons in C. elegans, 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 [1]. 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 [2]. 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.",
author = "Xinwei Yu and Creamer, {Matthew S.} and Francesco Randi and Sharma, {Anuj K.} and Linderman, {Scott W.} and Leifer, {Andrew M.}",
note = "Funding Information: We thank Eviatar Yemini and Oliver Hobert of Columbia University for strain OH15262. We acknowledge productive discussions with John Murray of University of Pennsylvania. This work used computing resources from the Princeton Institute for Computational Science and Engineering. Research reported in this work was supported by the Simons Foundation under awards SCGB #543003 to AML and SCGB #697092 to SWL; by the National Science Foundation, through an NSF CAREER Award to AML (IOS-1845137) and through the Center for the Physics of Biological Function (PHY-1734030); by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award numbers R21NS101629 to AML and 1R01NS113119 to SWL; and by the Swartz Foundation through the Swartz Fellowship for Theoretical Neuroscience to FR. Some strains are being distributed by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). Funding Information: Science and Engineering. Research reported in this work was supported by the Simons Foundation under awards SCGB #543003 to AML and SCGB #697092 to SWL; by the National Science Foundation, through an NSF CAREER Award to AML (IOS-1845137) and through the Center for the Physics of Biological Function (PHY-1734030); by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award numbers R21NS101629 to AML and 1R01NS113119 to SWL; and by the Swartz Foundation through the Swartz Fellowship for Theoretical Neuroscience to FR. Some strains are being distributed by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). Publisher Copyright: {\textcopyright} 2021, eLife Sciences Publications Ltd. All rights reserved.",
year = "2021",
month = jul,
doi = "10.7554/eLife.66410",
language = "English (US)",
volume = "10",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",
}