The invention provides an impulsive neural network-based image feature describing and memorizing method. the method comprises steps: M normalized images are inputted, the layer number of the impulsive neural network is determined according to the size of the image, a
gradient direction at each pixel point is acquired when pretreatment is carried out on the images, the
gradient direction is discretized into a preset individual value, distribution of one of each preset value number of neurons in the first layer in the impulsive neural network is determined according to the discretized
gradient direction,
membrane potential of neurons in the second layer and the distribution condition of the neurons in the second layer are calculated according to the distribution condition of the neurons in the first layer, the distribution conditions of the neurons in all
layers are obtained, a connection weight of each layer of the impulsive neural network is adjusted according to a timing relationship for distribution of neurons in all
layers and a STDP (Spike Timing-dependent
Plasticity) rule, and the image features are described and memorized in a connection weight form. The method of the invention can describe and memorize images of various kinds, can completely restore an image, and also has an image classification function.