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DOI: 10.4230/DagRep.8.11.112
High Throughput Connectomics (Dagstuhl Seminar 18481)

Authors: Moritz Helmstaedter, Jeff Lichtman, and Nir Shavit

Published in: Dagstuhl Reports, Volume 8, Issue 11 (2019)

Abstract
The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s and has recently garnered general interest thanks to technical and computational advances that offer the possibility of mapping mammalian brains. However, modern connectomics produces `big data' that must be analyzed at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. This workshop will bring together key researchers in the field, and experts from related fields, in order to understand the problems at hand and provide new approaches towards the design of high throughput systems for mapping the micro-connectivity of the brain.
Cite as

Moritz Helmstaedter, Jeff Lichtman, and Nir Shavit. High Throughput Connectomics (Dagstuhl Seminar 18481). In Dagstuhl Reports, Volume 8, Issue 11, pp. 112-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{helmstaedter_et_al:DagRep.8.11.112,
  author =	{Helmstaedter, Moritz and Lichtman, Jeff and Shavit, Nir},
  title =	{{High Throughput Connectomics (Dagstuhl Seminar 18481)}},
  pages =	{112--138},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{11},
  editor =	{Helmstaedter, Moritz and Lichtman, Jeff and Shavit, Nir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.8.11.112},
  URN =		{urn:nbn:de:0030-drops-103588},
  doi =		{10.4230/DagRep.8.11.112},
  annote =	{Keywords: Big Data, Connectomics, Distributed Computing, Machine Learning, Parallel Computing}
}
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