Research on Image Recognition Algorithm Based on Spiking Neural Networks

With the continuous development of artificial intelligence and computer vision technology, Artificial Neural Networks (ANNs) have achieved remarkable results in image recognition tasks. However, with the development of deep learning technology, the number of layers of neural networks is increasing and the training power consumption of the models is huge. Spiking Neural Network (SNN) is a kind of neural network that simulates the working mechanism of biological nervous system, which is characterized by the fact that the information transfer between neurons occurs in the form of pulses, and it has higher computational efficiency, stronger spatio-temporal information processing capability, and better bio-interpretability compared with traditional artificial neural networks. This paper analyzes the technical advantages of spiking neural network for image recognition, simulation experiments on spiking neural network in terms of data preprocessing, spiking coding, gradient substitution with reference to the classical image recognition model, and puts forward the proposed measures for the image recognition algorithm based on spiking neural network based on the experimental results.