The nervous system is an extremely large, strongly coupled, and highly nonlinear complex network with multiple scales, ranging from molecules, cells, ensembles to brain regions. In order to deeply explore the electrophysiological phenomena in neural activity of this complex system, traditional neuronal dynamics aims to carry out dynamic modeling, behavior and mechanism analysis to understand the neural information processing and cognitive functions from the perspective of dynamics.
The advancement of neuroimaging techniques together with electrophysiological methods are powerful ways to characterize neural activity. In recent decades, a variety of imaging strategies have emerged, such as brain functional magnetic resonance imaging, near-infrared imaging, and cutting edge fluorescence microscopy techniques are becoming promising tools for neuroscience research.
Various types of neural activity data obtained from these tools have spawned the generalized neuronal dynamics. These vast amounts of spatio-temporal data with high-dimensional place higher demands on data analysis methods, and there are many challenges in extracting the hidden features from the deluge of experimental data and properly modeling the colorful neural behaviors.
Actually, as a typical interdisciplinary area, the neuroscience increasingly relies on rapidly developed data analysis methods and network modeling interpretation. Nowadays, nonlinear dynamics, complex networks and control science, and even game theory have received more and more attention, gradually become powerful tools to characterize the topological structure and the evolution of functional network in neuroscience.
By delving into the complex network structure and rich dynamic behavior in nervous complex system, we will unravel the mysteries hidden in nervous system profoundly, and elucidate the intrinsic mechanisms of neurocognitive and mental activity. Not only that, in-depth dynamical network analysis will be able to promote greatly the clinical exploration and understanding of the mechanisms underlying neurological diseases; especially, provide more quantitative assessment for clinical neurological diagnosis, and trigger effective methods for neurotherapy and rehabilitation.
Through original research articles, editorial, and review articles, this topic aims to present an interdisciplinary communication platform for sharing information and ideas about the analysis and modeling of neuroscience data.
We welcome submissions that focus on the data analysis and modeling, including but not limited to applying neurophysiology, data science, nonlinear dynamics, complex system, and network science to explore the promising frontier of molecular, cellular, and clinical neuroscience.
The nervous system is an extremely large, strongly coupled, and highly nonlinear complex network with multiple scales, ranging from molecules, cells, ensembles to brain regions. In order to deeply explore the electrophysiological phenomena in neural activity of this complex system, traditional neuronal dynamics aims to carry out dynamic modeling, behavior and mechanism analysis to understand the neural information processing and cognitive functions from the perspective of dynamics.
The advancement of neuroimaging techniques together with electrophysiological methods are powerful ways to characterize neural activity. In recent decades, a variety of imaging strategies have emerged, such as brain functional magnetic resonance imaging, near-infrared imaging, and cutting edge fluorescence microscopy techniques are becoming promising tools for neuroscience research.
Various types of neural activity data obtained from these tools have spawned the generalized neuronal dynamics. These vast amounts of spatio-temporal data with high-dimensional place higher demands on data analysis methods, and there are many challenges in extracting the hidden features from the deluge of experimental data and properly modeling the colorful neural behaviors.
Actually, as a typical interdisciplinary area, the neuroscience increasingly relies on rapidly developed data analysis methods and network modeling interpretation. Nowadays, nonlinear dynamics, complex networks and control science, and even game theory have received more and more attention, gradually become powerful tools to characterize the topological structure and the evolution of functional network in neuroscience.
By delving into the complex network structure and rich dynamic behavior in nervous complex system, we will unravel the mysteries hidden in nervous system profoundly, and elucidate the intrinsic mechanisms of neurocognitive and mental activity. Not only that, in-depth dynamical network analysis will be able to promote greatly the clinical exploration and understanding of the mechanisms underlying neurological diseases; especially, provide more quantitative assessment for clinical neurological diagnosis, and trigger effective methods for neurotherapy and rehabilitation.
Through original research articles, editorial, and review articles, this topic aims to present an interdisciplinary communication platform for sharing information and ideas about the analysis and modeling of neuroscience data.
We welcome submissions that focus on the data analysis and modeling, including but not limited to applying neurophysiology, data science, nonlinear dynamics, complex system, and network science to explore the promising frontier of molecular, cellular, and clinical neuroscience.
