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International Symposium on Neural Networks

ISNN 2013: Advances in Neural Networks – ISNN 2013 pp 9–18Cite as

Generation and Analysis of 3D Virtual Neurons Using Genetic Regulatory Network Model

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7951)

Abstract

Neuronal morphology is significant for understanding structure-function relationships and brain information processing in computational neuroscience. So it is very important to simulate neuronal morphology completely and accurately. In this paper, we present a novel approach for efficient generation of 3D virtual neurons using genetic regulatory network model. This approach describes dendritic geometry and topology by locally inter-correlating morphological variables which can be represented by the dynamics of gene expression. The experimental results show that the generating virtual neurons that are anatomically indistinguishable and accurate from experimentally traced real neurons.

Keywords

  • virtual neuron
  • neuronal morphology
  • genetic regulatory network

This research is supported by the National Natural Science Foundation of China under Grant No. 61165002, and the Natural Science Foundation of Gansu Province of China under Grant No. 1010RJZA019.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xianghong Lin & Zhiqiang Li

Authors
  1. Xianghong Lin
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  2. Zhiqiang Li
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Editor information

Editors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, A 530, Chuangxinyuan Building, 116023, Dalian, China

    Chengan Guo

  2. Institute of Automation, Chinese Academy of Sciences, 100864, Beijing, China

    Zeng-Guang Hou

  3. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Zhigang Zeng

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Lin, X., Li, Z. (2013). Generation and Analysis of 3D Virtual Neurons Using Genetic Regulatory Network Model. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-39065-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39064-7

  • Online ISBN: 978-3-642-39065-4

  • eBook Packages: Computer ScienceComputer Science (R0)