Image classification method of spiking learning model based on dynamic threshold

Abstract

The invention discloses an image classification method of a spiking learning model based on a dynamic threshold. The method comprises the following steps: obtaining an image data set, converting imageinformation into a pulse excitation sequence by adopting a phase delay coding method, establishing and training a spiking learning model based on a dynamic threshold, and performing classification processing on images to be classified by utilizing the trained spiking learning model. According to the invention, image information is converted into a pulse excitation sequence by using a phase delaycoding method; according to the image classification method and device, the dynamic threshold is set up, the spiking learning model based on the dynamic threshold is set up for training, the trained spiking learning model is used for image classification, the robustness of the learning model can be remarkably improved while the training efficiency and accuracy of the learning model are guaranteed,and the image classification efficiency is further improved.

Description

technical field [0001] The invention belongs to the technical field of image classification, in particular to an image classification method based on a dynamic threshold-based spiking learning model. Background technique [0002] Since neural activity based on spiking timing has been found in different regions of the brain, including the retina, lateral geniculate nucleus, and visual cortex, more and more people have begun to pay attention to spiking neural networks based on temporal coding. Theory shows that the spiking neural network, which is the third generation neural network, has more powerful computing power and wider application prospects than the second generation neural network. However, the current application of spiking neurons as a model is relatively simple, and one of the main reasons is the lack of efficient and robust learning algorithms. [0003] Through the efforts of researchers, many achievements have been made in improving the learning algorithm effici...

AI Technical Summary

Problems solved by technology

However, for noises that have not been encountered or are more intense, its noise immunity will be reduced sharply

Therefore, how to improve the noise immunity of spiking neural networks is still a major challenge in this field.

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