Open AccessAutomatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set
Author(s) -
Julia Buhmann,
Arlo Sheridan,
Caroline Malin-Mayor,
Philipp Schlegel,
Stephan Gerhard,
Tom Kazimiers,
Renate Krause,
Tri Nguyen,
Larissa Heinrich,
Wei-Chung Allen Lee,
Rachel I. Wilson,
Stephan Saalfeld,
Gregory Jefferis,
Davi D. Bock,
Srinivas C. Turaga,
Matthew Cook,
Jan Funke
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-021-01183-7
Subject(s) - computer science , identification (biology) , electron microscope , on the fly , microscopy , artificial intelligence , annotation , set (abstract data type) , neuroscience , biological system , biology , physics , optics , programming language , operating system , botany
We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.