Memristor-Based Neural Network Implementation with Adjustable Synaptic Weights in LTSPICE

The memristors are innovative electronic elements with nano-sized structure and with very good memory and switching abilities. They have very low power consumption and a good compatibility to CMOS integrated chips, and they could be used in neural networks, memories, and many other schematics. In this paper an LTSPICE model of artificial neural network with memristor-based synapses is proposed. In this network, each synapse is realized with only one memristor, thus providing a higher reduction in circuit complexity and with main benefit of that individual memristor resistance value can be adjusted with external control voltage signals. The summing and scaling component implementations are based on op-amps and memristors. We use the most common logarithmic-sigmoidal activation function and it is realized by a voltage-controlled source. The operation of the proposed memristor neural network is analyzed and simulated in both L TSPICE and MATLAB, and the derived results are compared and verified successfully. The proposed memristor-based neural network is a significant step for engineering low power complex networks in very high-density integrated circuits and chips.