Analysis of dynamical behaviors for delayed neural networks with inverse Lipschitz neuron activations and impulses

This paper develops a novel class of neural networks with inverse Lipschitz neuron activations and impulses. Based on the topological degree theory and matrix inequality techniques, we study the existence and uniqueness of equilibrium point of the neural network. By constructing suitable Lyapunov functions, a sufficient condition ensuring global exponential stability of the neural network is presented. Finally, two numerical examples further illustrate the correctness of the results obtained in this paper.