<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8087</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Theory for the Emergence of Neocortical Network Architecture</title>
    <parentTitle language="eng">BioRxiv</parentTitle>
    <identifier type="doi">https://doi.org/10.1101/2020.11.13.381087</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Daniel Udvary</author>
    <submitter>Daniel Baum</submitter>
    <author>Philipp Harth</author>
    <author>Jakob H. Macke</author>
    <author>Hans-Christian Hege</author>
    <author>Christiaan P. J. de Kock</author>
    <author>Bert Sakmann</author>
    <author>Marcel Oberlaender</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="NeuroConnect">NeuroConnect</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="PredictingCorticalConnectomes">PredictingCorticalConnectomes</collection>
  </doc>
  <doc>
    <id>5170</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>129</issue>
    <volume>8</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of dense statistical connectomes from sparse morphological data</title>
    <parentTitle language="eng">Frontiers in Neuroanatomy</parentTitle>
    <identifier type="doi">10.3389/fnana.2014.00129</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-53075</enrichment>
    <author>Robert Egger</author>
    <submitter>Vincent J. Dercksen</submitter>
    <author>Vincent J. Dercksen</author>
    <author>Daniel Udvary</author>
    <author>Hans-Christian Hege</author>
    <author>Marcel Oberlaender</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="visalgo">Visual Data Analysis in Science and Engineering</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="Neuro">Neuro</collection>
    <collection role="projects" number="NeuroConnect">NeuroConnect</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>5307</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2014-10-11</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of dense statistical connectomes from sparse morphological data</title>
    <abstract language="eng">Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and sub-cellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions&#13;
of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-53075</identifier>
    <identifier type="doi">10.3389/fnana.2014.00129</identifier>
    <enrichment key="SourceTitle">Appeared in: Frontiers in Neuroanatomy 8:129 (2014)</enrichment>
    <author>Robert Egger</author>
    <submitter>Vincent J. Dercksen</submitter>
    <author>Vincent J. Dercksen</author>
    <author>Daniel Udvary</author>
    <author>Hans-Christian Hege</author>
    <author>Marcel Oberlaender</author>
    <series>
      <title>ZIB-Report</title>
      <number>14-43</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dense connectome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reconstruction</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="Neuro">Neuro</collection>
    <collection role="projects" number="NeuroConnect">NeuroConnect</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/5307/EggerDenseConnectomesPreprint.pdf</file>
  </doc>
</export-example>
