Attractor and boundedness for stochastic Cohen–Grossberg neural networks with delays

By employing Lyapunov method and Lasalle-type theorem, the attractor of stochastic Cohen–Grossberg neural networks (CGNN) with delays is initially investigated. Novel results and sufficient criteria on the attractor of stochastic CGNN are obtained. The almost surely asymptotic stability is a special case of our results. The boundedness of stochastic CGNN is also investigated. Finally, one example is presented to illustrate the correctness and effectiveness of our theoretical results.