Manuscript Topics

Neural networks have received considerable attention on account of their wide applications in various areas such as image processing, signal processing, associative memory, pattern classification, optimization, and moving object speed detection. Furthermore, in the world in which we live today, time delay commonly exists due to the finite speed of transmission and network congestion, which is always considered as the impact factor for the instability of a network. On the other hand, the diffusion phenomena could not be ignored in neural networks and electric circuits once electrons transport in a nonuniform electromagnetic field. In view of this, it is challenging to analyze and discuss time-delayed neural networks with and without reaction-diffusion.

This special issue aims to collect original and quality contributions on more recent developments of dynamic analysis and scientific application of time-delay neural networks. Topics of interest include, but are not limited to:
• Analysis for dynamical behaviors of time-delay neural networks
• Deep learning or control for time-delay neural networks
• Scientific application of time-delay neural networks based on their dynamics
• Dynamic analysis of time-delay neural networks with reaction-diffusion
• Deep learning or control for time-delay neural networks with reaction-diffusion
• Scientific application of time-delay neural networks with reaction-diffusion

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