Evolutionary Selection of Network Structure and Function
Evolutionary Selection of Network Structure and Function
复制标题
网络结构和功能的进化选择
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
S. Dougherty
中科院分区：
文献类型：
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作者：
L. Yaeger;O. Sporns;Steven Williams;Xin Shuai;S. Dougherty
我们探索进化的神经网络的结构和功能之间的关系，通过应用图论工具的拓扑结构的人工神经网络的分析，已知表现出进化的动态神经复杂性的增加。我们的结果表明，由于物理限制（例如布线长度和大脑体积）而出现的网络结构与在没有物理限制的情况下纯粹为了功能而进化的最佳网络拓扑之间存在协同收敛。我们观察到的聚类系数的增加与路径长度的减少，共同产生了一个驱动的进化偏向小世界网络相对于可比的网络在一个被动的零模型。这些小世界偏差在进化积极选择增加神经复杂性的同一时期（也是模型的代理在行为上适应环境的时期）表现出来，从而加强了小世界网络结构和复杂神经动力学之间的联系。
We explore the relationship between evolved neural network structure and function, by applying graph theoretical tool s to the analysis of the topology of artificial neural networks known to exhibit evolutionary increases in dynamical neural complexity. Our results suggest a synergistic convergence between network structures emerging due to physical constraints, such as wiring length and brain volume, and optimal network topologies evolved purely for function in the absence of physical constraints. We observe increases in clusterin g coefficients in concert with decreases in path lengths that together produce a driven evolutionary bias towards smallworld networks relative to comparable networks in a passive null model. These small-world biases are exhibited during the same periods that evolution actively selects for increa sing neural complexity (also during which the model’s agents are behaviorally adapting to their environment), thus strengt hening the association between small-world network structures and complex neural dynamics.