Global exponential periodicity of three-unit neural networks in a ring with time-varying delays

This paper formulates and studies a model of three-unit neural networks in a ring. The model can well describe many practical architectures of delayed neural networks, which is generalization of some existing neural networks under a time-varying environment. Without assuming the boundedness, monotonicity, and differentiability of activation functions and any symmetry of interconnections, we establish some sufficient conditions for checking the existence of periodic solution and global exponential stability for the neural networks. A continuation theorem of the coincidence degree and inequality analysis are employed. Our results are all independent of the delays and maybe more convenient to design a circuit network.