A Memristive Neural Network Model With Associative Memory for Modeling Affections

Abstract

Memristor is a nonlinear resistor with memory, which has the characteristics of neuron synapses and can be used to design a new generation of memristive neural networks. Based on the Pavlov associative memory, a novel memristive associative memory neural network model is designed by using the charge-controlled nanoscale HP memristor model as the electronic synapse. This model includes neurons, memristor synapses, and weighted input feedback learning rule. Based on the proposed memristive associative memory model, a memristor-based neural network structure for simulating human emotions is further designed. The emotion simulation takes into account the excitation and inhibition between different neurons, making it more bionic. In order to simulate the memristive neural network structure, a relatively simplified emotional simulation circuit is constructed, which effectively reduces the network complexity and circuit power consumption. Finally, PSPICE is used to simulate the circuit. The simulation results not only verify the correctness of the model but also achieve a simple simulation of human emotions, which is helpful for the further development of the artificial neural network in the field of artificial intelligence.