Fast deep learning correspondence for neuron tracking and identification in C.elegans using syntheti...
Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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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-traine...
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Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
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TN_cdi_proquest_journals_2479575089
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2479575089
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2101.08211
