About this Research Topic
This Research Topic aims to provide the academic community with a forum to present and discuss the latest theoretical and applied research related to recent advances in synchronization and multistability in neural networks. Many experimental results of neuroimaging of the brain can be interpreted in terms of complex high-level networks who’s mathematical modeling is based on high-dimensional systems of nonlinear differential equations. Thus, nonlinear dynamics is the basis for a rigorous description of the behavior of large-scale neural networks. An interesting application of the theory of nonlinear dynamical systems to neuroscience is the study of the phenomena of the central nervous system, which exhibits almost discontinuous transitions between metastable states of the brain. The focus is on advancing modern understanding of the origins of multistability and metastability in neural networks, as well as the mechanisms underlying transitions between coexisting partial synchronization states. We will also try to explain how multi- and metastability at different levels of brain organization changes as a result of adaptation, cognition, neurological diseases, and ageing processes.
We invite original and review papers covering new physical and mathematical methods, biologically plausible models, innovative approaches, and novel important techniques that could lead to significant advances in understanding of synchronization and multistability in neural network dynamics. The topics of interest include, but are not limited to the following issues:
- Deterministic Neural Models
- Stochastic Neural Models
- Discrete Neural Models
- Continuous Neural Models
Keywords: tractor, neural network, multistability, metastability, synchronization, nonlinear dynamics, chaos, noise
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.