<?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>3642</id>
    <completedYear>2012</completedYear>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2375</pageFirst>
    <pageLast>2391</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>22</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex</title>
    <parentTitle language="eng">Cerebral Cortex</parentTitle>
    <identifier type="doi">doi:10.1093/cercor/bhr317</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Marcel Oberlaender</author>
    <author>Christiaan P. J. de Kock</author>
    <author>Randy M. Bruno</author>
    <author>Alejandro Ramirez</author>
    <author>Hanno Meyer</author>
    <author>Vincent J. Dercksen</author>
    <author>Moritz Helmstaedter</author>
    <author>Bert Sakmann</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="projects" number="Neuro">Neuro</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>3744</id>
    <completedYear>2009</completedYear>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>other</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex</title>
    <parentTitle language="eng">Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Marcel Oberlaender</author>
    <author>Randy M. Bruno</author>
    <author>Christiaan P. J. de Kock</author>
    <author>Hanno Meyer</author>
    <author>Vincent J. Dercksen</author>
    <author>Bert Sakmann</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="projects" number="Neuro">Neuro</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>8632</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>39</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-04-12</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The Impact of Neuron Morphology on Cortical Network Architecture</title>
    <abstract language="eng">The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density and cellular diversity of the neuropil. The higher these three factors are the more recurrent is the topology of the network. Conversely, the lower these factors are the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular and network scales from the specific morphological properties of its neuronal constituents.</abstract>
    <parentTitle language="eng">Cell Reports</parentTitle>
    <identifier type="doi">10.1016/j.celrep.2022.110677</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-03-22</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Daniel Udvary</author>
    <submitter>Philipp Harth</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="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>
</export-example>
