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

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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Bibliographic Details
Main Authors: Xinwei Yu, Matthew S Creamer, Francesco Randi, Anuj K Sharma, Scott W Linderman, Andrew M Leifer
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2021-07-01
Series:eLife
Subjects:
computer vision
deep learning
artificial neural network
tracking
registration
transformer
Online Access:https://elifesciences.org/articles/66410

Internet

https://elifesciences.org/articles/66410

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