Advancements in neuroscience research have led to significant progress in understanding the complex dynamics of the brain. However, the nonlinear properties of brain function still present a challenge in understanding the complex interactions between neural elements.
Time series measurements offer an opportunity to analyze the nonlinear dynamics of the brain and to gain insights into the underlying mechanisms governing brain function. The analysis of nonlinear dynamics of brain function from time series measurements presents an exciting research opportunity for scientists seeking to expand current knowledge in neuroinformatics.
The goal of this Research Topic is to provide an opportunity for researchers to explore the dynamics of the brain from a nonlinear perspective using time series measurements. The aim is to encourage a better understanding of the complex interactions within neural networks that give rise to brain function. The research will aim to address the problem of the lack of understanding of the underlying principles that govern non-linear dynamics in the brain and ways to measure, analyze, and interpret the complex interactions that occur.
The scope of this Research Topic includes but is not limited to the use of various computational, statistical, and mathematical models in analyzing the nonlinear dynamics of brain function from time series measurements.
We encourage authors to submit manuscripts focusing on the following themes:
Techniques for analyzing nonlinear time series data in the brain
Applications and Implications of nonlinear dynamics in brain function and pathology
Theoretical and experimental approaches to studying nonlinear dynamics in brain function
Integrating nonlinear dynamics with other approaches to brain function analysis
We welcome original research, review articles, perspective pieces, and opinions that address the nonlinear dynamics of brain function from time series measurements.
Advancements in neuroscience research have led to significant progress in understanding the complex dynamics of the brain. However, the nonlinear properties of brain function still present a challenge in understanding the complex interactions between neural elements.
Time series measurements offer an opportunity to analyze the nonlinear dynamics of the brain and to gain insights into the underlying mechanisms governing brain function. The analysis of nonlinear dynamics of brain function from time series measurements presents an exciting research opportunity for scientists seeking to expand current knowledge in neuroinformatics.
The goal of this Research Topic is to provide an opportunity for researchers to explore the dynamics of the brain from a nonlinear perspective using time series measurements. The aim is to encourage a better understanding of the complex interactions within neural networks that give rise to brain function. The research will aim to address the problem of the lack of understanding of the underlying principles that govern non-linear dynamics in the brain and ways to measure, analyze, and interpret the complex interactions that occur.
The scope of this Research Topic includes but is not limited to the use of various computational, statistical, and mathematical models in analyzing the nonlinear dynamics of brain function from time series measurements.
We encourage authors to submit manuscripts focusing on the following themes:
Techniques for analyzing nonlinear time series data in the brain
Applications and Implications of nonlinear dynamics in brain function and pathology
Theoretical and experimental approaches to studying nonlinear dynamics in brain function
Integrating nonlinear dynamics with other approaches to brain function analysis
We welcome original research, review articles, perspective pieces, and opinions that address the nonlinear dynamics of brain function from time series measurements.
