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Synaptic Partner Assignment Using Attentional Voxel Association Networks

Author(s): Turner, Nicholas L; Lee, Kisuk; Lu, Ran; Wu, Jingpeng; Ih, Dodam; et al

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Abstract: Connectomics aims to recover a complete set of synaptic connections within a dataset imaged by volume electron microscopy. Many systems have been proposed for locating synapses, and recent research has included a way to identify the synaptic partners that communicate at a synaptic cleft. We reframe the problem of identifying synaptic partners as directly generating the mask of the synaptic partners from a given cleft. We train a convolutional network to perform this task. The network takes the local image context and a binary mask representing a single cleft as input. It is trained to produce two binary output masks: one which labels the voxels of the presynaptic partner within the input image, and another similar labeling for the postsynaptic partner. The cleft mask acts as an attentional gating signal for the network. We find that an implementation of this approach performs well on a dataset of mouse somatosensory cortex, and evaluate it as part of a combined system to predict both clefts and connections.
Publication Date: 2020
Citation: Turner, Nicholas L., Kisuk Lee, Ran Lu, Jingpeng Wu, Dodam Ih, and H. Sebastian Seung. "Synaptic Partner Assignment Using Attentional Voxel Association Networks." 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (2020): pp. 1209-1213. doi:10.1109/ISBI45749.2020.9098489
DOI: 10.1109/ISBI45749.2020.9098489
ISSN: 1945-7928
EISSN: 1945-8452
Pages: 1209 - 1213
Type of Material: Conference Article
Journal/Proceeding Title: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Version: Author's manuscript

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