基于STDP 规则和忆阻桥突触的神经网络及图像处理

Abstract

Memristor is a nonlinear circuit element which has property of memory and the similar synapse characteristic. Based on these properties, we presents a memristor bridge synaptic circuit based on the STDP (spike-time-dependent plasticity) learning rule which has an advantage that can be used as a synapse in the arti cial neural network. According to the advantage, we combine the new circuit with other circuits and networks and the bran-new circuits and networks are constructed. Firstly, we combine the memristor bridge synaptic circuit with three additional transistor components and the synaptic calculation of the neural network is realized, and then the complete memristor bridge synaptic neural network is constructed. Secondly, we combine the cellular neural network with it and can be used in image denoising, edge extraction, corner detection and Chinese character recognition. Finally, the feasibility of the proposed method is proved by the a series of the simulation experiments, and the memristor bridge synaptic neural network based on the STDP learning rule has more bionic feasibility, more integrated and more easy to replace the template are demonstrated, and it is hopeful to solve the real-time complex intelligent problems.