@article {65738, title = {The mind of a mouse.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 Sep 17}, pages = {1372-1376}, abstract = {

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain{\textquoteright}s synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.08.010}, author = {Abbott, Larry F and Bock, Davi D and Callaway, Edward M and Denk, Winfried and Dulac, Catherine and Fairhall, Adrienne L and Fiete, Ila and Harris, Kristen M and Helmstaedter, Moritz and Jain, Viren and Kasthuri, Narayanan and LeCun, Yann and Lichtman, Jeff W and Littlewood, Peter B and Luo, Liqun and Maunsell, John H R and Reid, R Clay and Rosen, Bruce R and Rubin, Gerald M and Sejnowski, Terrence J and Seung, H Sebastian and Svoboda, Karel and Tank, David W and Tsao, Doris and Van Essen, David C} } @conference {3858, title = {Boundary learning by optimization with topological constraints.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2010}, month = {2010}, abstract = {

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

}, author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian} } @article {1235, title = {Ultrastructural analysis of hippocampal neuropil from the connectomics perspective.}, journal = {Neuron}, volume = {67}, year = {2010}, month = {2010 Sep 23}, pages = {1009-20}, abstract = {

Complete reconstructions of vertebrate neuronal circuits on the synaptic level require new approaches. Here, serial section transmission electron microscopy was automated to densely reconstruct four volumes, totaling 670 μm(3), from the rat hippocampus as proving grounds to determine when axo-dendritic proximities predict synapses. First, in contrast with Peters{\textquoteright} rule, the density of axons within reach of dendritic spines did not predict synaptic density along dendrites because the fraction of axons making synapses was variable. Second, an axo-dendritic touch did not predict a synapse; nevertheless, the density of synapses along a hippocampal dendrite appeared to be a universal fraction, 0.2, of the density of touches. Finally, the largest touch between an axonal bouton and spine indicated the site of actual synapses with about 80\% precision but would miss about half of all synapses. Thus, it will be difficult to predict synaptic connectivity using data sets missing ultrastructural details that distinguish between axo-dendritic touches and bona fide synapses.

}, keywords = {Animals, Automatic Data Processing, Axons, Dendrites, Electron, Hippocampus, Long-Evans, Male, Microscopy, Neuropil, Newborn, Rats, Synapses, Transmission}, issn = {1097-4199}, doi = {10.1371/journal.pcbi.1001066}, author = {Mishchenko, Yuriy and Hu, Tao and Spacek, Josef and Mendenhall, John and Harris, Kristen M and Chklovskii, Dmitri B} }