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DOI: 10.4230/DagRep.8.11.112
URN: urn:nbn:de:0030-drops-103588
URL: http://drops.dagstuhl.de/opus/volltexte/2019/10358/
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Helmstaedter, Moritz ; Lichtman, Jeff ; Shavit, Nir
Weitere Beteiligte (Hrsg. etc.): Moritz Helmstaedter and Jeff Lichtman and Nir Shavit

High Throughput Connectomics (Dagstuhl Seminar 18481)

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dagrep_v008_i011_p112_18481.pdf (3 MB)


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.

BibTeX - Entry

@Article{helmstaedter_et_al:DR:2019:10358,
  author =	{Moritz Helmstaedter and Jeff Lichtman and Nir Shavit},
  title =	{{High Throughput Connectomics (Dagstuhl Seminar 18481)}},
  pages =	{112--138},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{11},
  editor =	{Moritz Helmstaedter and Jeff Lichtman and Nir Shavit},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10358},
  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}
}

Keywords: Big Data, Connectomics, Distributed Computing, Machine Learning, Parallel Computing
Seminar: Dagstuhl Reports, Volume 8, Issue 11
Issue Date: 2019
Date of publication: 10.04.2019


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