Perisomatic Features Enable Efficient and Dataset Wide Cell-Type Classifications Across Large-Scale Electron Microscopy Volumes
Leila Elabbady(Allen Institute for Brain Science), Forrest Collman(Allen Institute for Brain Science), Manuel Castro(Princeton University), Sergiy Popovych(Princeton University), JoAnn Buchanan(Allen Institute for Brain Science), Szi-chieh Yu(Princeton University), Nuno Maçarico da Costa(Allen Institute for Brain Science), Thomas Macrina(Princeton University), William S. Wong(Princeton University), Russel Torres(Harvard University), Sven Dorkenwald(Google (United States)), Shanka Subhra Mondal(Princeton University), Nicholas L. Turner(Princeton University), H. Sebastian Seung(Princeton University), William Silversmith(Princeton University), Nico Kemnitz(Princeton University), Jingpeng Wu(Princeton University), Sam Kinn(Allen Institute for Brain Science), Marc Takeno(Allen Institute for Brain Science), Daniel J. Bumbarger(Allen Institute for Brain Science), Shang Mu(Princeton University), Akhilesh Halageri(Princeton University), Casey M Schneider-Mizell(Allen Institute for Brain Science), Eric Mitchell(Princeton University), Gayathri Mahalingam(Allen Institute for Brain Science), Dan Kapner(Allen Institute for Brain Science), Sharmishtaa Seshamani(Allen Institute for Brain Science), Barak Nehoran(Princeton University), Derrick Brittain(Allen Institute for Brain Science), Kai Li(Princeton University), J. Alexander Bae(Korea Advanced Institute of Science and Technology), Wenjing Yin(Allen Institute for Brain Science), Kisuk Lee(Princeton University), Zhen Jia(Princeton University), Ran Lu(Princeton University), Ágnes L. Bodor(Allen Institute for Brain Science), Chris Jordan(Princeton University), R. Clay Reid(Allen Institute for Brain Science)
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