6,553 research outputs found
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial
section electron microscopy. Image analysis has been performed by manual labor
for half a century, and efforts at automation date back almost as far.
Convolutional nets were first applied to neuronal boundary detection a dozen
years ago, and have now achieved impressive accuracy on clean images. Robust
handling of image defects is a major outstanding challenge. Convolutional nets
are also being employed for other tasks in neural circuit reconstruction:
finding synapses and identifying synaptic partners, extending or pruning
neuronal reconstructions, and aligning serial section images to create a 3D
image stack. Computational systems are being engineered to handle petavoxel
images of cubic millimeter brain volumes
Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics
We propose a neural connectomics strategy called Fluorescent In-Situ
Sequencing of Barcoded Individual Neuronal Connections (FISSEQ-BOINC),
leveraging fluorescent in situ nucleic acid sequencing in fixed tissue
(FISSEQ). FISSEQ-BOINC exhibits different properties from BOINC, which relies
on bulk nucleic acid sequencing. FISSEQ-BOINC could become a scalable approach
for mapping whole-mammalian-brain connectomes with rich molecular annotations
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