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The purpose of this paper is to develop a methodology for the stochastically asymptotic synchronization problem for a class of neutral-type chaotic neural networks with both leakage delay and Markovian jumping parameters under impulsive perturbations.

The authors perform drive-response concept and time-delay feedback control techniques to investigate a class of neutral-type chaotic neural networks with both leakage delay and Markovian jumping parameters under impulsive perturbations. New sufficient criterion is established without strict conditions imposed on the activation functions.

It turns out that the approach results in new sufficient criterion easy to be verified but without the usual assumption of the differentiability and monotonicity of the activation functions. Two examples show the effectiveness of the obtained results.

The novelty of the proposed approach lies in removing the usual assumption of the differentiability and monotonicity of the activation functions, and the use of the Lyapunov functional method, Jensen integral inequality, a novel Gu’s lemma, reciprocal convex and linear convex combination technique for the stochastically asymptotic synchronization problem for a class of neutral-type chaotic neural networks with both leakage delay and Markovian jumping parameters under impulsive perturbations.

The purpose of this paper is to develop a methodology for the existence and global exponential stability of the unique equilibrium point of a class of impulsive Cohen‐Grossberg neural networks.

The authors perform M‐matrix theory and homeomorphism mapping principle to investigate a class of impulsive Cohen‐Grossberg networks with time‐varying delays and distributed delays. The approach builds on new sufficient criterion without strict conditions imposed on self‐regulation functions.

The authors' approach results in new sufficient criteria easy to verify but without the usual assumption that the activation functions are bounded and the time‐varying delays are differentiable. An example shows the effectiveness and superiority of the obtained results over some previously known results.

The novelty of the proposed approach lies in removing the usual assumption that the activation functions are bounded and the time‐varying delays are differentiable, and the use of M‐matrix theory and homeomorphism mapping principle for the existence and global exponential stability of the unique equilibrium point of a class of impulsive Cohen‐Grossberg neural networks.