Annotating synapses in large EM datasets.

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arXiv. 2014 Sep 5;:arXiv:1409.1801 [q-bio.QM]

Annotating synapses in large EM datasets. Scheffer LabFlyEMScientific Computing Software

Plaza SM, Parag T, Huang G, Olbris DJ, Saunders MA, Rivlin PK

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Abstract

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders.

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Janelia Authors

Gary Huang

Donald Olbris

Pat Rivlin

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Annotating synapses in large EM datasets.

Abstract

Janelia Authors