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.
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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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
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