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Fully memristive spiking-neuron learning framework, which uses drift anddiffusion memristor models as axon and dendrite respectively, becomes a hottopic recently with the development of memristor devices. Normally, some otherdevices like resistor or capacitor are still necessary on recent works of fullymemristive learning framework. However, theoretically, one neuron needs axonand dendrite only, which makes technique process simpler and learning frameworkmore similar to biologic brain. In this paper, a fully memristivespiking-neuron learning framework is introduced, in which a neuron structure isjust built of one drift and one diffusion memristive models. To verify itmerits, a feedforward neural network for pattern recognition and a cellularneural network for edge detection are designed. Experiment results show thatcompared to other memristive neural networks, our framework's the processingspeed is much faster and the hardware resource is saved in pattern recognitiondue to its simple structure. Further due to the dynamic filtering function ofdiffusion memristor model in our learning framework, its peak signal noiseratio (PSNR) is much higher than traditional algorithms in edge detection.

Fully Memristive Spiking-Neuron Learning Framework and its Applications on Pattern Recognition and Edge Detection