Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
- Princeton University, United States
- Stanford University, United States
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 10ms making it suitable for future real-time applications.
Data availability
All datasets generated as part of this work have been deposited in a public Open Science Foundation repository DOI:10.17605/OSF.IO/T7DZU.
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fDKLC_Neuron_ID_C.elegansOpen Science Foundation, DOI 10.17605/OSF.IO/T7DZU.
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Tracking Neurons in a Moving and Deforming Brain DatasetIEEE DataPorts DOI:10.21227/H2901H.
Article and author information
Author details
Anuj Kumar Sharma Ph.D.
Andrew Michael Leifer
Funding
Simons Foundation (543003)
- Andrew Michael Leifer
Simons Foundation (697092)
- Scott W Linderman
National Science Foundation (IOS-184537)
- Andrew Michael Leifer
National Science Foundation (PHY-1734030)
- Andrew Michael Leifer
National Institutes of Health (R21NS101629)
- Andrew Michael Leifer
National Institutes of Health (1R01NS113119)
- Scott W Linderman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Yu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
eLife 10:e66410.
https://doi.org/10.7554/eLife.66410
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