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DFG-RSF: Complex dynamical networks: effects of heterogeneity, adaptivity, and delays

DFG-RSF：复杂动态网络：异质性、适应性和延迟的影响

基本信息

批准号：
308748074
负责人：
Professor Dr. Eckehard Schöll
金额：
--
依托单位：
Forschungsbereich Komplexitätsforschung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/308748074?language=en
关键词：
DFG RSF Complex dynamical networks

项目摘要

Two types of network models have been mostly considered so far in the literature: static and dynamic networks. In static networks, only the structure of connections is of importance. In contrast, in dynamical networks the state of each unit depends on time, and its dynamics is influenced by the interaction with other units. In spite of the large number of interesting and significant results, the general theory of dynamical networks is only at the stage of early development, which is due to the extreme complexity and broadness of the subject. This project addresses a fundamental problem of the theory of complex nonlinear dynamical networks. More specifically, it considers the following aspects of complexity: inhomogeneous topological structure, adaptive connections, and the presence of coupling delays. The types of networks to be studied include but are not limited to neural and neuromorphic networks, wireless sensor networks, and networks of electronic oscillators. The main objectives include: I. Development of the definition of the term ''complexity'' for dynamical networks, as well as the qualitative criteria and quantitative measures for their complexity. This direction is very important since the term ''complex network'' has not been strictly defined so far, although its wide use in nonlinear dynamics has long been calling for elaboration of its definition.II. Development of neuromorphic network models with complex modular structure and analytic methods of study of such models. We will construct models reproducing structural properties of real neuronal networks, primarily their modularity, and study the link between the connectivity characteristics and their function. III. Study of the influence of coupling delays and adaptivity of connections on the dynamics of pulse-coupled networks of phase oscillators. The main objective is to study the suppression of the global synchronization and emergence of phase patterns and other complex dynamical regimes. We aim to understand how delayed coupling and plasticity affect the bifurcation mechanisms for the desynchronization in pulse-coupled systems. IV. Development and study of electronic prototypes of complex networks with coupling delays demonstrating nontrivial dynamical regimes. Particularly, we plan to implement a network of oscillators interacting via long chains of excitable units and demonstrating novel types of chimera-like behavior.

两种类型的网络模型已被认为是迄今为止在文献中：静态和动态网络。在静态网络中，只有连接的结构是重要的。相反，在动态网络中，每个单元的状态依赖于时间，其动态性受到与其他单元相互作用的影响。尽管有大量有趣和重要的结果，动力网络的一般理论只是处于早期发展阶段，这是由于该主题的极端复杂性和广泛性。这个项目解决了复杂非线性动态网络理论的一个基本问题。更具体地说，它考虑了以下方面的复杂性：不均匀的拓扑结构，自适应连接，耦合延迟的存在。要研究的网络类型包括但不限于神经和神经形态网络、无线传感器网络和电子振荡器网络。主要目标包括：一。发展动态网络“复杂性”的定义，以及其复杂性的定性标准和定量度量。这个方向是非常重要的，因为“复杂网络”这个术语迄今为止还没有严格定义，尽管它在非线性动力学中的广泛应用一直要求对其定义进行详细说明。具有复杂模块结构的神经形态网络模型的开发和研究此类模型的分析方法。我们将构建再现真实的神经元网络的结构特性的模型，主要是它们的模块性，并研究连接特性与它们的功能之间的联系。三.连接的耦合延迟和自适应性对相位振荡器脉冲耦合网络动力学的影响的研究。主要目的是研究全局同步的抑制和相位图案的出现以及其他复杂的动力学机制。我们的目标是了解延迟耦合和可塑性如何影响脉冲耦合系统中的去混沌分岔机制。四.发展与研究具耦合延迟之复杂网路之电子原型，以展现非平凡动力学机制。特别是，我们计划实现一个振荡器网络，通过可兴奋单元的长链相互作用，并展示新型的嵌合体样行为。