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Automated synaptic connectivity inference for volume electron microscopy
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DOI：10.1038/nmeth.4206.png)
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Abstract
En 中文
Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.
Keywords:
PROJECTION NEURONS
NEURAL ACTIVITY
DIRECTION-SELECTIVITY
WIRING SPECIFICITY
CEREBRAL-CORTEX
AREA-X
RECONSTRUCTION
CIRCUIT
SEGMENTATION
SYNAPSES
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