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  <title>NITRC News Group Forum: morphological-homogeneity-of-neurons--searching-for-outlier-neuronal-cells</title>
  <link>http://www.nitrc.org/forum/forum.php?forum_id=3170</link>
  <description>&lt;p class=&quot;abstract&quot;&gt;&lt;div class=&quot;Abstract&quot; lang=&quot;en&quot;&gt;&lt;a name=&quot;Abs1&quot;&gt;&lt;/a&gt;&lt;span class=&quot;AbstractHeading&quot;&gt;Abstract&amp;nbsp;&amp;nbsp;&lt;/span&gt;&lt;div class=&quot;normal&quot;&gt;We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the
 &lt;i&gt;NeuroMorpho.org&lt;/i&gt; database, with more than 5,000&amp;nbsp;neurons. Each neuron in a given analysis is represented by a feature vector composed of 20
 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel
 density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest
 probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential
 of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal
 species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and
 they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations
 involving one or more categories of cells, as well as for detection of new categories and possible artifacts.
 &lt;/div&gt;
 &lt;/div&gt;&lt;/p&gt;&lt;ul&gt;
	&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Content Type &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;Journal Article&lt;/span&gt;&lt;/li&gt;&lt;li&gt;Category Original Article&lt;/li&gt;&lt;li&gt;Pages 1-11&lt;/li&gt;&lt;li&gt;DOI 10.1007/s12021-012-9150-5&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Authors&lt;/span&gt;&lt;ul&gt;
		&lt;li&gt;Krissia Zawadzki, Institute of Physics at São Carlos, University of São Paulo, PO Box 369, São Carlos, SP CEP 13.560-970, Brazil&lt;/li&gt;&lt;li&gt;Christoph Feenders, School of Computing Science, Newcastle University, Claremont Tower, Newcastle-upon-Tyne, NE1 7RU UK&lt;/li&gt;&lt;li&gt;Matheus P. Viana, Institute of Physics at São Carlos, University of São Paulo, PO Box 369, São Carlos, SP CEP 13.560-970, Brazil&lt;/li&gt;&lt;li&gt;Marcus Kaiser, School of Computing Science, Newcastle University, Claremont Tower, Newcastle-upon-Tyne, NE1 7RU UK&lt;/li&gt;&lt;li&gt;Luciano da F. Costa, Institute of Physics at São Carlos, University of São Paulo, PO Box 369, São Carlos, SP CEP 13.560-970, Brazil&lt;/li&gt;
	&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;ul class=&quot;parents&quot;&gt;
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		&lt;li&gt;&lt;span class=&quot;header labelName&quot;&gt;Journal &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;&lt;a href=&quot;http://www.springerlink.com/content/120559/&quot;&gt;Neuroinformatics&lt;/a&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Online ISSN &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;1559-0089&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Print ISSN &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;1539-2791&lt;/span&gt;&lt;/li&gt;
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