A memristive neural network with features of asymmetric coexisting attractors and large-scale amplitude control

It is a universally acknowledged fact that memristor is widely used in neural networks owing to its memory functions similar to synapses. This paper aims to construct a memristive neural network (MNN) with special dynamic behaviors and structure, which consists of four cyclic neurons and one unidirectional memristive synapse. In this study, we explored the dynamic behaviors, including asymmetric coexisting attractors and parameter-relied large-scale amplitude control. Specially, we found that there are four different types of asymmetric coexisting attractors, namely coexisting double-point (or periodic or chaotic) attractors and coexisting periodic and chaotic attractors. In order to reveal the characteristics of large-scale amplitude control, we used analysis methods such as phase plane plots and time sequences. The existence of this phenomenon is closely related to system parameters and initial values. Meanwhile, a specific circuit experiment is implemented to verify the feasibility of our designation.