Mathematical modeling for evolution of heterogeneous modules in the brain
Mathematical modeling for evolution of heterogeneous modules in the brain
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DOI：
10.1016/j.neunet.2014.07.013
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发表时间：
2015-02
期刊：
影响因子：
--
通讯作者：
Y. Yamaguti;I. Tsuda
中科院分区：
文献类型：
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作者：
Y. Yamaguti;I. Tsuda
在哺乳动物大脑的大多数皮层区域都发现了模块化结构，但对其进化起源知之甚少。一些研究人员提出，子系统之间的信息传输最大化可以作为理解复杂大脑网络发展的原则。在本文中，我们研究如何异构模块开发耦合映射网络通过遗传算法，选择是基于最大限度地提高双向信息传输。两个功能分化模块从两个具有随机耦合的均质系统演化而来，这与系统内和系统间耦合的对称性破缺有关。通过对网络参数空间中最优参数值的探索，发现最优网络存在于非相干态失去稳定性的过渡点附近，在该过渡点处出现极慢的振荡运动。
Modular architecture has been found in most cortical areas of mammalian brains, but little is known about its evolutionary origin. It has been proposed by several researchers that maximizing information transmission among subsystems can be used as a principle for understanding the development of complex brain networks. In this paper, we study how heterogeneous modules develop in coupled-map networks via a genetic algorithm, where selection is based on maximizing bidirectional information transmission. Two functionally differentiated modules evolved from two homogeneous systems with random couplings, which are associated with symmetry breaking of intrasystem and intersystem couplings. By exploring the parameter space of the network around the optimal parameter values, it was found that the optimum network exists near transition points, at which the incoherent state loses its stability and an extremely slow oscillatory motion emerges.