  Fully memristive spiking-neuron learning framework, which uses drift and
diffusion memristor models as axon and dendrite respectively, becomes a hot
topic recently with the development of memristor devices. Normally, some other
devices like resistor or capacitor are still necessary on recent works of fully
memristive learning framework. However, theoretically, one neuron needs axon
and dendrite only, which makes technique process simpler and learning framework
more similar to biologic 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. To verify it
merits, a feedforward neural network for pattern recognition and a cellular
neural network for edge detection are designed. Experiment results show that
compared to other memristive neural networks, our framework's the processing
speed is much faster and the hardware resource is saved in pattern recognition
due to its simple structure. 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.
