Selective Recruitment in Hierarchical Complex Dynamical Networks with Linear-Threshold Rate Dynamics
Selective Recruitment in Hierarchical Complex Dynamical Networks with Linear-Threshold Rate Dynamics
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DOI：
10.1109/cdc.2018.8618734
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Erfan Nozari;J. Cortés
中科院分区：
文献类型：
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作者：
Erfan Nozari;J. Cortés
了解大脑的复杂网络动力学如何支持认知构成了系统和控制理论之前最具挑战性和影响力的问题之一。在本文中，我们研究了选择性招聘问题，即同时选择性抑制活动在一个子网络和自上而下的招聘另一个认知更高级别的子网络，使用类的线性阈值率（LTR）模型。首先，我们使用奇异摄动理论提供了一个理论框架，在双层分层LTR网络中使用反馈和前馈控制的选择性招聘。然后，我们将此框架推广到任意数量的层，并提供条件的子网络，保证同时选择性抑制和自上而下的招聘在所有层的联合结构。最后，我们说明了这个框架的应用程序在一个生物启发的情况下，同时稳定和控制的较低水平的兴奋性子网络是通过适当的振荡活动，在较高水平的抑制性子网络。
Understanding how the complex network dynamics of the brain support cognition constitutes one of the most challenging and impactful problems ahead of systems and control theory. In this paper, we study the problem of selective recruitment, namely, the simultaneous selective inhibition of activity in one subnetwork and top-down recruitment of another by a cognitively-higher level subnetwork, using the class of linear-threshold rate (LTR) models. We first use singular perturbation theory to provide a theoretical framework for selective recruitment in a bilayer hierarchical LTR network using both feedback and feedforward control. We then generalize this framework to arbitrary number of layers and provide conditions on the joint structure of subnetworks that guarantee simultaneous selective inhibition and top-down recruitment at all layers. We finally illustrate an application of this framework in a biologically-inspired scenario where simultaneous stabilization and control of a lower level excitatory subnetwork is achieved through proper oscillatory activity in a higher level inhibitory subnetwork.