@article {46327,
	title = {Space-time wiring specificity supports direction selectivity in the retina.},
	journal = {Nature},
	volume = {509},
	year = {2014},
	month = {2014 May 15},
	pages = {331-6},
	abstract = {<p>How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of {\textquoteright}citizen neuroscientists{\textquoteright}. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such {\textquoteright}space-time wiring specificity{\textquoteright} could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.</p>
},
	keywords = {Amacrine Cells, Animals, Artificial Intelligence, Brain Mapping, Crowdsourcing, Dendrites, Mice, Models, Neurological, Motion, Neural Pathways, Presynaptic Terminals, Retina, Retinal Bipolar Cells, Spatio-Temporal Analysis},
	issn = {1476-4687},
	doi = {10.1038/nature13240},
	author = {Kim, Jinseop S and Greene, Matthew J and Zlateski, Aleksandar and Lee, Kisuk and Richardson, Mark and Turaga, Srinivas C and Purcaro, Michael and Balkam, Matthew and Robinson, Amy and Behabadi, Bardia F and Campos, Michael and Denk, Winfried and Seung, H Sebastian}
}
@conference {3858,
	title = {Boundary learning by optimization with topological constraints.},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	month = {2010},
	abstract = {<p>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.</p>
},
	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}
}
