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  <title>NITRC News Group Forum: employing-neugen-2.0-to-automatically-generate-realistic-morphologies-of-hippocampal-neurons-and-neural-networks-in-3d</title>
  <link>http://www.nitrc.org/forum/forum.php?forum_id=3637</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;Detailed cell and network morphologies are becoming increasingly important in Computational Neuroscience. Great efforts have
 been undertaken to systematically record and store the anatomical data of cells. This effort is visible in databases, such
 as &lt;i&gt;NeuroMorpho.org&lt;/i&gt;. In order to make use of these fast growing data within computational models of networks, it is vital to include detailed
 data of morphologies when generating those cell and network geometries. For this purpose we developed the Neuron Network Generator
 &lt;i&gt;NeuGen 2.0&lt;/i&gt;, that is designed to include known and published anatomical data of cells and to automatically generate large networks of
 neurons. It offers export functionality to classic simulators, such as the NEURON Simulator by Hines and Carnevale (&lt;cite&gt;2003&lt;/cite&gt;). &lt;i&gt;NeuGen 2.0&lt;/i&gt; is designed in a modular way, so any new and available data can be included into &lt;i&gt;NeuGen 2.0&lt;/i&gt;. Also, new brain areas and cell types can be defined with the possibility of constructing user-defined cell types and networks.
 Therefore, &lt;i&gt;NeuGen 2.0&lt;/i&gt; is a software package that grows with each new piece of anatomical data, which subsequently will continue to increase the
 morphological detail of automatically generated networks. In this paper we introduce &lt;i&gt;NeuGen 2.0&lt;/i&gt; and apply its functionalities to the CA1 hippocampus. Runtime and memory benchmarks show that &lt;i&gt;NeuGen 2.0&lt;/i&gt; is applicable to generating very large networks, with high morphological detail.
 &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 1-12&lt;/li&gt;&lt;li&gt;DOI 10.1007/s12021-012-9170-1&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Authors&lt;/span&gt;&lt;ul&gt;
		&lt;li&gt;S. Wolf, Goethe Center for Scientific Computing, Goethe University Frankfurt, Kettenhofweg 139, 60325 Frankfurt, Germany&lt;/li&gt;&lt;li&gt;S. Grein, Goethe Center for Scientific Computing, Goethe University Frankfurt, Kettenhofweg 139, 60325 Frankfurt, Germany&lt;/li&gt;&lt;li&gt;G. Queisser, Goethe Center for Scientific Computing, Goethe University Frankfurt, Kettenhofweg 139, 60325 Frankfurt, Germany&lt;/li&gt;
	&lt;/ul&gt;&lt;/li&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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