<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>8768</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace>Vienna, Austria</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Stratification Matrix Viewer for Analysis of Neural Network Data</title>
    <abstract language="eng">The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.</abstract>
    <parentTitle language="eng">Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)</parentTitle>
    <identifier type="doi">10.2312/vcbm.20221194</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-08-31</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Philipp Harth</author>
    <submitter>Philipp Harth</submitter>
    <author>Sumit Vohra</author>
    <author>Daniel Udvary</author>
    <author>Marcel Oberlaender</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="baum">Baum, Daniel</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="persons" number="vohra">Vohra, Sumit Kumar</collection>
    <collection role="projects" number="PredictingCorticalConnectomes">PredictingCorticalConnectomes</collection>
    <thesisPublisher>Zuse Institute Berlin (ZIB)</thesisPublisher>
  </doc>
</export-example>
