Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists.
Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists.
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DOI：
10.3389/fncom.2011.00011
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发表时间：
2011
影响因子：
3.2
通讯作者：
McDonnell MD
中科院分区：
文献类型：
--
作者：
Prettejohn BJ;Berryman MJ;McDonnell MD
在计算神经科学中，许多网络的模拟都假设是完全同质的随机网络，即埃尔德什-雷尼类型的网络，或者规则网络，尽管人们已经认识到解剖学上的大脑网络在连接性上更复杂，例如，可以表现出“无标度”和“小世界”的特性。我们回顾了最著名的算法，用于构建具有非齐次统计特性的网络，并提供了简单的伪代码，用于在软件模拟中再现这种网络。我们还回顾了一些有用的数学结果和近似的统计描述这些网络模型，包括度分布，平均路径长度和聚类系数。我们演示了如何使用这样的结果作为部分验证和验证的实现。最后，我们讨论了一个有时被忽视的建模选择，它对模拟网络的性质至关重要：网络的有向性。最著名的网络算法产生无向网络，我们强调这一点，强调如何简单的适应，而不是产生有向网络。
Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.
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