Fully memristive spiking-neuron learning framework and its applications on pattern recognition and edge detection

Abstract Fully memristive neuron learning framework, which uses drift and diffusion memristor models to build an artificial neuron structure, becomes a hot topic recently with the development of memristor. However, some other devices like resistor or capacitor are still necessary in recent works of fully memristive learning framework. Theoretically, if one neuron is built by memristors only, the technique process will be simpler and learning framework will be more like biological brain. In this paper, a fully memristive spiking-neuron learning framework is introduced, in which a neuron structure is just built of one drift and one diffusion memristive models and spikes are used as transmission signals. The learning framework and spiking coding mode are simple and direct without any complicated calculation on hardware. To verify its merits, a feedforward neural network for pattern recognition and a cellular neural network for edge detection are designed. Experimental results show that compared to other memristive neural networks, processing speed of the proposed framework is very high, and the hardware resource is saved in pattern recognition. Further, due to the dynamic filtering function of diffusion memristor model in our learning framework, its peak signal noise ratio (PSNR) is much higher than traditional algorithms in edge detection.