An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks
An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks
复制标题
DOI：
10.1016/j.neucom.2017.08.005
复制
发表时间：
2018-01
期刊：
影响因子：
6
通讯作者：
Xianghong Lin;Zhiqiang Li;Huifang Ma;Xiangwen Wang
中科院分区：
文献类型：
--
作者：
Xianghong Lin;Zhiqiang Li;Huifang Ma;Xiangwen Wang
在计算神经科学中，神经元形态的计算建模对于理解结构-功能关系和脑信息处理具有重要意义。利用基因调控网络模型，提出了一种高效生成三维虚拟神经元的进化发展方法。这种方法通过局部相互关联的形态变量来描述树突形态的发展过程，这些变量可以用基因表达的动态来表示。然后，采用基因分段复制和发散算子的多目标进化算法对虚拟神经元进行进化，目的是生成与实验跟踪的真实神经元一样好的统计形态测量结果。我们通过实验生成运动神经元，并通过测量一系列新出现的形态特征来统计比较真实神经元和生成的虚拟神经元之间的差异。结果表明，生成的虚拟神经元看起来逼真、准确，进一步表明该方法是了解神经发育，特别是研究神经元结构与功能关系的有效工具。
Computational modeling of neuronal morphologies is significant for understanding structure-function relationships and brain information processing in computational neuroscience. Using a gene regulatory network model, an evolutionary developmental approach is presented for efficient generation of 3D virtual neurons. This approach describes the developmental process of dendritic morphologies by locally inter-correlating morphological variables which can be represented by the dynamics of gene expression. Then, the multi-objective evolutionary algorithm with gene segmental duplication and divergence operators is applied to evolve the virtual neurons, which aims at generating virtual neurons that are as good as the experimentally traced real neurons in terms of statistical morphological measurements. We experimentally generated motoneurons and statistically compared between the real neurons and the generated virtual neurons by measuring a series of emergent morphological features. The results show that the generated virtual neurons are seemingly realistic, accurate, and further suggest that this approach is an efficient tool for understanding neural development and investigating the relation of neuronal structure to function in particular.