About this Research Topic
Many systems in nature and in theory display long-lasting but transient states, referred to as metastable states. Examples in physiology include traveling waves or firing rate patterns across the cerebral cortex under either spontaneous or stimulus-evoked activity, cardiac arrhythmias, and pattern formation in cells. In several cases, the studied systems can be modeled as networks of interacting units, or they themselves belong to a wider network.
The ubiquity and possible advantages (and disadvantages) of metastability have led to a strong interest in the topic across different fields. This has led to the development of research in various areas, but many open questions still remain, such as
1. which dynamical mechanisms can lead to metastability,
2. how metastability can be generated or prevented (e.g via traditional modeling or machine learning),
3. how metastability can be identified in experiments (looking at new data or re-visiting previous data),
4. what advantages metastability can confer to a system.
It is clear that addressing these questions requires coordinated expertise from a variety of fields, such as dynamical systems theory, neuroscience, machine learning, control theory, and data analysis. This Research Topic aims to achieve this by selecting important contributions on the topic of metastable dynamics in networks with potential applications to physiology using knowledge from any field of research. Authors are encouraged to identify connections between their own field and others and discuss insights gained from these connections. Contributions may be achieved in various forms. New findings or methodologies can be reported via Original Research or Methods articles. We also welcome Reviews or Perspectives with deep insights into this interdisciplinary topic.
The ubiquity and possible advantages (and disadvantages) of metastability have led to a strong interest in the topic across different fields. This has led to the development of research in various areas, but many open questions still remain, such as
1. which dynamical mechanisms can lead to metastability,
2. how metastability can be generated or prevented (e.g via traditional modeling or machine learning),
3. how metastability can be identified in experiments (looking at new data or re-visiting previous data),
4. what advantages metastability can confer to a system.
It is clear that addressing these questions requires coordinated expertise from a variety of fields, such as dynamical systems theory, neuroscience, machine learning, control theory, and data analysis. This Research Topic aims to achieve this by selecting important contributions on the topic of metastable dynamics in networks with potential applications to physiology using knowledge from any field of research. Authors are encouraged to identify connections between their own field and others and discuss insights gained from these connections. Contributions may be achieved in various forms. New findings or methodologies can be reported via Original Research or Methods articles. We also welcome Reviews or Perspectives with deep insights into this interdisciplinary topic.
Keywords: network physiology, Metastability, transients, network, metastable, brain, arrhythmias, seizures, variability, dynamics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
