@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} } @article {6261, title = {Connectomic reconstruction of the inner plexiform layer in the mouse retina.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 8}, pages = {168-74}, abstract = {

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer{\textendash}the main computational neuropil region in the mammalian retina{\textendash}the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.

}, keywords = {Amacrine Cells, Animals, Biological, Cell Communication, Computer-Assisted, Connectome, Electron, Image Processing, Inbred C57BL, Mice, Microscopy, Models, Neuropil, Retina, Retinal Ganglion Cells}, issn = {1476-4687}, doi = {10.1038/nature12346}, author = {Helmstaedter, Moritz and Briggman, Kevin L and Turaga, Srinivas C and Jain, Viren and Seung, H Sebastian and Denk, Winfried} } @article {4326, title = {Large-scale automated histology in the pursuit of connectomes.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {31}, year = {2011}, month = {2011 Nov 9}, pages = {16125-38}, abstract = {

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain{\textquoteright}s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4077-11.2011}, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, Davi D and Briggman, Kevin L and Chklovskii, Dmitri B and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John P and Lee, Wei-Chung Allen and Meyer, Hanno S and Micheva, Kristina D and Oberlaender, Marcel and Prohaska, Steffen and Reid, R Clay and Smith, Stephen J and Takemura, Shinya and Tsai, Philbert S and Sakmann, Bert} } @article {3844, title = {Convolutional networks can learn to generate affinity graphs for image segmentation.}, journal = {Neural Computation}, volume = {22}, year = {2010}, month = {2010 Feb}, pages = {511-38}, abstract = {

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Processing, Mathematical Computing, Mathematical Concepts, Mathematics, Microscopy, Neural Networks (Computer), Pattern Recognition}, issn = {1530-888X}, doi = {10.1162/neco.2009.10-08-881}, author = {Turaga, Srinivas C and Murray, Joseph F and Jain, Viren and Roth, Fabian and Helmstaedter, Moritz and Briggman, Kevin and Denk, Winfried and Seung, Hyunjune Sebastian} }