Abstract: We present an automated method to track and identify neurons in C. elegans,
called "fast Deep Learning Correspondence" or fDLC, based on the transformer
network architecture. The model is trained once on empirically derived
synthetic data and then predicts neural correspondence across held-out real
animals via transfer learning. 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
. Using only position information, the method achieves 80.0% accuracy at
tracking neurons within an individual and 65.8% accuracy at identifying neurons
across individuals. Accuracy is even higher on a published dataset .
Accuracy reaches 76.5% when using color information from NeuroPAL. Unlike
previous methods, fDLC 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.