Neuronal networks are high-dimensional graphs that are packed into three-dimensional nervous tissue at extremely high density. Comprehensively mapping these networks is therefore a major challenge. Although recent developments in volume electron microscopy imaging have made data acquisition feasible for circuits comprising a few hundreds to a few thousands of neurons, data analysis is massively lagging behind. The aim of this Perspective is to summarize and quantify the challenges for data analysis in cellular-resolution connectomics and describe current solutions involving online crowd-sourcing and machine-learning approaches.
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Acknowledgements
I am grateful to the members of my laboratory for many fruitful discussions, specifically to K.M. Boergens, Y. Buckley, F. Isensee, N. Marahori, A. Mohn and H. Wissler for help with generating figures, and E. Dow for discussions concerning game development. I thank M. Berning, K.M. Boergens, E. Dow and A. Schaefer for helpful comments on the manuscript.
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Helmstaedter, M. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat Methods 10, 501–507 (2013). https://doi.org/10.1038/nmeth.2476
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DOI: https://doi.org/10.1038/nmeth.2476
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