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Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset
- Publication Year :
- 2019
- Publisher :
- Cold Spring Harbor Laboratory, 2019.
-
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.
- Subjects :
- 0303 health sciences
business.industry
Computer science
Pattern recognition
Convolutional neural network
030218 nuclear medicine & medical imaging
Synapse
03 medical and health sciences
Identification (information)
0302 clinical medicine
medicine.anatomical_structure
Biological neural network
medicine
Artificial intelligence
Neuron
business
030304 developmental biology
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....6dfd2acbd2e311495b0a81478855aa22
- Full Text :
- https://doi.org/10.1101/2019.12.12.874172