Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

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 re...

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Detalles Bibliográficos
Autores principales: Yu, Xinwei, Creamer, Matthew S, Randi, Francesco, Sharma, Anuj K, Linderman, Scott W, Leifer, Andrew M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367385/
https://www.ncbi.nlm.nih.gov/pubmed/34259623
http://dx.doi.org/10.7554/eLife.66410

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367385/
https://www.ncbi.nlm.nih.gov/pubmed/34259623
http://dx.doi.org/10.7554/eLife.66410

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