Improved Learning Experience Memristor Model and Application as Neural Network Synapse - Xiaohong Zhang (IEEE Access, 2019)
This paper proposes a memristor model, named learning experience memristor (LEM), for using as synapse in the associative neural network. The properties of LEM are discussed under different external voltages. And then, we design a new feedback learning rule, all input feedback (AIF). An associative neural network-based the AIF law and LEM synapse is constructed and analyzed, and the associative neural network incorporates learning experience behavior, forgetting, and threshold functions. The properties of LEM are also verified through PSpice simulation. The associative neural network circuit based on AIF law and LEM are constructed and simulated using PSpice, the simulation results are analyzed sufficiently. Finally, different memristors are used as synapses in the associative neural network, and we analyze and compare the simulation results. All simulation results show that the associative neural network incorporating LEM synapses and AIF learning law exhibits good performance, mimicking biological neural networks, and self-learning behavior.