Dongil Lee

TL;DR: In this paper, the authors describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many subcellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database.

TL;DR: In this paper, the authors used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections from the temporal lobe of the cerebral cortex.

TL;DR: The RhoanaNet image processing pipeline was developed to automatically segment large volumes of EM data and ease the burden of manual proofreading and annotation and every stage of the software pipeline was updated to provide better throughput performance and higher quality segmentation results.

TL;DR: A workflow from tissue harvest to imaging that will generate a 2 petabyte dataset (> 300,000,000 images) of rat visual cortex imaged at a 4 nm x 4nm x-y (Nyquist sampling of membranes) and 30nm section thickness in less than 6 months is described.

TL;DR: The ability to compare visual quantity arises spontaneously in untrained networks Distinct tunings to measure proportion and difference of quantities are observed and it is found that a slight difference in the nonlinearity profile determines the type of measure.