Manuscript Topics

The nervous system is an extremely large and complex network with multiple scales from molecules, cells, ensembles to brain regions, and with multiple levels from high-level center including cerebrum, cerebellum, basal ganglia, etc. to low-level center like spinal cord. To deeply explore the neural information processing and the functional mechanism of this complex system, neural dynamical modeling has played a big role in studying neural behaviors, neural cognitions, neural disease, etc. Nowadays, with the rapid development of big data and AI, new tools and challenges arise for neuroscience research. On the one hand, big data and AI can be powerful tools to characterize the topological structure and the evolution of the functional network of neural systems. In-depth dynamical network analysis will be able to promote the understanding of the mechanisms underlying neurological diseases, provide more quantitative assessment for clinical neurological diagnosis, and trigger effective methods for neurotherapy and rehabilitation. On the other hand, by delving into the complex network structure and rich dynamic behaviors in the nervous system, we can unravel the mysteries hidden in it, and elucidate the intrinsic neural mechanisms of sensory, cognition, memory and motion. Neural dynamics research on the mechanism of biological intelligence can provide new principles, new ways and new methods for brain-inspired intelligence, and further improve intelligence technology. A major challenge in neuroscience will be to learn from the nervous structure and information processing mechanism, vigorously promote the integration of artificial intelligence and biological intelligence, and carry out in-depth research on brain-inspired 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 control science to explore the promising frontier of molecular, cellular, clinical neuroscience and neural engineering.

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 neurological diseases
- Modeling of multi-objective brain decision making
- Learning of target motion in spiking neural networks
- Cooperative control of multi-agent system based on neural network
- Intelligent computing inspired by neural circuits
- Intelligent motion control inspired by nervous system

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