Journal
ELIFE
Volume 10, Issue -, Pages -Publisher
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.66410
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Funding
- Princeton Institute for Computational Science and Engineering
- Simons Foundation [543003, 697092]
- National Science Foundation, through an NSF CAREER Award [IOS-1845137, PHY-1734030]
- National Institute of Neurological Disorders and Stroke of the National Institutes of Health [R21NS101629, R01NS113119]
- Swartz Foundation - NIH Office of Research Infrastructure Programs [P40 OD010440]
- Eviatar Yemini and Oliver Hobert of Columbia University for strain [OH15262]
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A method called fDNC, based on the transformer network architecture, is proposed for automated tracking and identifying neurons in C. elegans. This method predicts neural correspondence quickly without requiring straightening or transforming the animal into a canonical coordinate system, making it suitable for future real-time applications.
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 (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.
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