Collection

Nonlinear Dynamics Modeling and Control for Brain Science and Brain-Like Intelligence

Submission status
Closed
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. 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. 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 the functional network in neuroscience. 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. By delving into the complex network structure and rich dynamic behavior in the nervous complex system, we will unravel the mysteries hidden in the nervous system profoundly, and elucidate the intrinsic mechanisms of neurocognitive and mental activity. Neural dynamics research on the mechanism of biological intelligence can provide new principles, new ways and new methods for brain-like intelligence, and play an important role in the dynamics and control of brain-like intelligence systems. The current artificial intelligence technology mainly relies on big data based on a large number of samples to train neural networks and deep learning algorithms to imitate brain functions to work, and its information processing mechanism is often different from the working process of the brain. A major challenge in neuroscience will be to learn from the brain nerve structure and information processing mechanism, vigorously promote the integration of artificial intelligence and biological intelligence, and carry out in-depth research on brain-like intelligence, including the establishment of brain-like information coding, processing, memory, learning, and reasoning theories and algorithms. This Special Issue addresses specifically this objective and aims to present an interdisciplinary communication platform for sharing information and ideas about neuroscience data analysis and modeling, neurological diagnosis information, effective new methods of neurotherapy and rehabilitation, and brain-like intelligence. 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. Some of the highlighted topics are: - Dynamic modeling and control of neurological diseases - Neural information processing and cognitive function exploration - Classification and prediction of epilepsy based on machine learning - Clinical EEG data analysis and dynamic modeling of epilepsy - Modeling of multi-objective brain decision model based on reinforcement learning - Reinforcement learning of target motion in spike neural networks - Cooperative control of multi-agent system based on neural network

Editors

  • Dr. Qingyun Wang Dr. Qingyun Wang , 

    Dr. Qingyun Wang

    Professor, Department of Dynamics and Control, Beihang University, Beijing, China

  • Dr. Fang Han Dr. Fang Han  &

    Dr. Fang Han

    Professor, College of Information Science and Technology, Donghua University, Shanghai, China

  • Prof. Jianzhong Su Prof. Jianzhong Su

    Prof. Jianzhong Su

    Department of Mathematics, University of Texas at Arlington, USA

Articles (7 in this collection)