NeuGen 2.0 - Automatic generation of large neuron networks using anatomical data bases

Abstract

Event Abstract Back to Event NeuGen 2.0 - Automatic generation of large neuron networks using anatomical data bases Sergei Wolf1*, Stefan Grein1 and Gillian Queisser1 1 University of Frankfurt, Germany In Computational Neuroscience the simulation of single cells and neuron networks is becoming increasingly dependent on detailed morphology descriptions on the cell level. Great efforts have been undertaken to systematically record and store the anatomical data of cells. This effort is visible in data bases, such as NeuroMorpho.org. In order to make use of these fast growing data within computational models of networks, it is vital to include it when generating cell morphologies and network geometries. For this purpose we developed the Neuron Network Generator NeuGen 2.0, 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. NeuGen 2.0 is designed in a modular way, so any new and available data can be included into NeuGen 2.0. Also, new brain areas and cell types can be defined and advanced by the user. Therefore, NeuGen 2.0 is a software package that grows with every new piece of anatomical data, which subsequently will continue to increase the morphological detail of automatically generated networks. Keywords: computational neuroscience, Neural Networks (Computer), Software Development, Software packaging, Database and tools developement Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Wolf S, Grein S and Queisser G (2014). NeuGen 2.0 - Automatic generation of large neuron networks using anatomical data bases. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00098 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Sergei Wolf, University of Frankfurt, Frankfurt, Germany, sergei.wolf@gcsc.uni-frankfurt.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sergei Wolf Stefan Grein Gillian Queisser Google Sergei Wolf Stefan Grein Gillian Queisser Google Scholar Sergei Wolf Stefan Grein Gillian Queisser PubMed Sergei Wolf Stefan Grein Gillian Queisser Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.