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

DFG-RSF：复杂动态网络：异质性、适应性和拓扑的影响

• 批准号: 440145547
• 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: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/440145547?language=en
• 关键词: DFG; RSF; Complex; dynamical; networks

The project is directed towards studying theoretically and numerically the dynamics of complex adaptive networks. In our focus is the interplay of adaptivity of the links, heterogeneity, and the topology of the network. The applications include but are not limited to neural and neuromorphic networks. In the framework of the continuation of the project, we will study the dynamics of networks of different types of smoothly or pulse-coupled oscillators with the additional feature of plasticity of connections in a general context. In particular, in this project we would like to understand how plasticity affects the (cluster-)synchronizability in networks of different complexity: complex network structures, heterogeneous dynamical nodes, non-smooth interactions. This might eventually also shed light on the role of plasticity for the temporal coding of information by neuronal spikes.

该项目旨在研究复杂自适应网络的理论和数值动力学。我们关注的是链路的适应性、异构性和网络拓扑结构之间的相互作用。应用包括但不限于神经和神经形态网络。在项目继续的框架内，我们将研究不同类型的平滑或脉冲耦合振荡器网络的动力学，并在一般情况下具有连接可塑性的附加特征。特别是，在这个项目中，我们想要了解可塑性如何影响不同复杂性网络中的（集群）同步性：复杂的网络结构，异构的动态节点，非光滑的相互作用。这也可能最终阐明可塑性在神经元尖峰信息的时间编码中的作用。