Perspectives on adaptive dynamical systems

Abstract

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Persistent and responsive collective motion with adaptive time delay

“…In this study, we propose adaptive time delay to effectively manage this persistence-responsivity tradeoff in collective motion. Adaptivity, which involves the ability to adapt to changes in external conditions, has been observed in diverse domains such as biology, physics, and social science (37). For the collective motion in nature, it has been found that fish groups can fine-tune their critical point in response to environmental risk and noise levels (38,39).…”

Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

“…In order to achieve maximum resource utilization, it is necessary to design reasonable dynamics for the links' prices (i.e., the edges' states) [20,21]. In biological neural networks, the facilitation of synapses can be seen as dynamic behavior of edges, where the adaptability of synaptic dynamics leads to the emergence of chaotic dynamic behaviors [22,23]. In transportation networks, reasonable paths (edges) are crucial for the stable operation of the transportation network [24,25].…”

Editorial: Advancing our understanding of the impact of dynamics at different spatiotemporal scales and structure on brain synchronous activity, volume II

“…Leveraging complex network analysis and dynamical modeling, researchers can investigate how alterations in brain network organization are related to neurological disorders (see e.g., Stam, 2014 ). Moreover, understanding the mechanisms by which synaptic adaptation influences network dynamics can provide insights into disease pathology and potential therapeutic targets (see e.g., Berner et al, 2023 ; Sawicki et al, 2023 ). Translational studies involving both human subjects and animal models, such as mice, facilitate the validation of findings across species and provide valuable insights into the generalizability of therapeutic interventions.…”