The models of competitive neural network (CNN) was in recent past proposed to describe the dynamics of cortical cognitive maps with unsupervised synaptic modifications, where there are two types of memories: Long-term memories (LTM) and short-term memories (STM), LTM presents unsupervised and slow synaptic modifications and STM characterize the fast neural activity. This paper is concerned with a class of neutral type CNN’s with mixed delay and D operator. By employing the appropriate differential inequality theory, some sufficient conditions are given to ensure that all solutions of the model converge exponentially to zero vector. Finally, an illustrative example is also given at the end of this paper to show the effectiveness of the proposed results.

最近提出了竞争神经网络（CNN）模型来描述具有无监督突触修饰的皮质认知图的动力学，其中存在两种类型的记忆：长期记忆（LTM）和短期记忆（STM） ，LTM表现出无监督和缓慢的突触修饰，而STM则表征了快速的神经活动。本文涉及一类具有混合延迟和D算子的中立型CNN。通过采用适当的微分不等式理论，给出了一些充分的条件以确保模型的所有解都以指数形式收敛到零向量。最后，本文末尾还给出了一个说明性示例，以说明所提出结果的有效性。