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Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset

Julia Buhmann, View ORCID ProfileArlo Sheridan, Stephan Gerhard, Renate Krause, Tri Nguyen, Larissa Heinrich, View ORCID ProfilePhilipp Schlegel, Wei-Chung Allen Lee, View ORCID ProfileRachel Wilson, View ORCID ProfileStephan Saalfeld, View ORCID ProfileGregory Jefferis, View ORCID ProfileDavi Bock, View ORCID ProfileSrinivas Turaga, Matthew Cook, View ORCID ProfileJan Funke
doi: https://doi.org/10.1101/2019.12.12.874172
Julia Buhmann
1HHMI Janelia, Ashburn, USA
2Institute of Neuroinformatics, Zurich, Switzerland
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Arlo Sheridan
1HHMI Janelia, Ashburn, USA
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Stephan Gerhard
3Harvard Medical School, Boston, USA
6UniDesign Solutions GmbH
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Renate Krause
2Institute of Neuroinformatics, Zurich, Switzerland
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Tri Nguyen
3Harvard Medical School, Boston, USA
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Larissa Heinrich
1HHMI Janelia, Ashburn, USA
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Philipp Schlegel
4MRC Laboratory of Molecular Biology, Cambridge, UK
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Wei-Chung Allen Lee
3Harvard Medical School, Boston, USA
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Rachel Wilson
3Harvard Medical School, Boston, USA
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Stephan Saalfeld
1HHMI Janelia, Ashburn, USA
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Gregory Jefferis
4MRC Laboratory of Molecular Biology, Cambridge, UK
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Davi Bock
5University of Vermont, Burlington, USA
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Srinivas Turaga
1HHMI Janelia, Ashburn, USA
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Matthew Cook
2Institute of Neuroinformatics, Zurich, Switzerland
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Jan Funke
1HHMI Janelia, Ashburn, USA
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  • For correspondence: funkej@janelia.hhmi.org
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Abstract

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and require only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: between 92% and 96% of edges linking connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 19, 2020.
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Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset
Julia Buhmann, Arlo Sheridan, Stephan Gerhard, Renate Krause, Tri Nguyen, Larissa Heinrich, Philipp Schlegel, Wei-Chung Allen Lee, Rachel Wilson, Stephan Saalfeld, Gregory Jefferis, Davi Bock, Srinivas Turaga, Matthew Cook, Jan Funke
bioRxiv 2019.12.12.874172; doi: https://doi.org/10.1101/2019.12.12.874172
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Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset
Julia Buhmann, Arlo Sheridan, Stephan Gerhard, Renate Krause, Tri Nguyen, Larissa Heinrich, Philipp Schlegel, Wei-Chung Allen Lee, Rachel Wilson, Stephan Saalfeld, Gregory Jefferis, Davi Bock, Srinivas Turaga, Matthew Cook, Jan Funke
bioRxiv 2019.12.12.874172; doi: https://doi.org/10.1101/2019.12.12.874172

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