Scientists have increasingly realized that they must rely on the rapid development of technologies and the substantial progress of theoretical neuroscience to revitalize neuroscience. They have realized that there should be a broad vision and exploration of neural activity in the whole brain from the perspective of evolution and from the perspective of large-scale neuroscience. All these studies are closely related to brain plasticity and neuroplasticity.

We find that there is an invisible but insurmountable gap between the research results of neuroscience at different levels, and the research results at different levels cannot be quoted, influenced, and fused with each other. In particular, in the field of computational neuroscience, the study of cognitive activity in the brain is patchy. This question is an obstacle to a breakthrough in general cognitive neuroscience. In particular, research on the mechanisms of consciousness, thinking, intelligence, prediction, and visual perception has been slow. There is little progress on the nature of creativity and memory storage and retrieval. This is a great challenge to the development of brain science today. The development of cognitive neuroscience is not only increasingly dependent on the progress of experimental technical means and strict experimental data, but also needs to understand and mine the principle of signal processing and transmission of brain networks from the theoretical height with quantitative methods and insights into the internal mechanism of neural coding distribution mode, so as to find the law and essence behind the vast experimental data.

The goal of this Special Issue is to increase knowledge of how the brain functions and to further understanding of the basis and prediction of various diseases of the brain, from the perspective of large-scale neuroscience. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Structural and functional brain network models and global neural coding based on neural plasticity
• Modelling analysis and clinical application of neural diseases based on neural plasticity
• The mode of work and corresponding energy consumption of the default mode network that generates spontaneous activity when the brain is in an at rest state
• The role and contribution of default mode network to the formation of cognitive neural networks
• Feedback mechanisms of mechanical force signal to neural information processing in the nervous system
• Neurodynamic model of cerebral artery angiodynamics, blood flow change, and neural network coupling
• Neuronal firing model coupled with glial cell and capillary mechanics
• Prediction and diagnosis of multiple types of cognitive dysfunction by fatigue and injury models of cerebral cortical vessels
• Nonlinear dynamical analysis of firing mode of coupled neuron network system
• Deep learning and neural plasticity in brain network models
• Intelligent computing in brain networks
• Application of neural plasticity in human-computer fusion systems