Image Recognition Algorithm Based on Spiking Neural Network

Xiao Fei, Liao Jianping, Tian Jie, Wang Guangshuo
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Abstract

Image recognition is one of the basic tasks of computer vision, and it is also one of the important research directions in the field of machine learning. The artificial neural network algorithm has achieved very remarkable results in image recognition, convolutional neural network is one of the most popular artificial neural network, it’s also the main solution to image recognition currently. The spiking neural network is called the third-generation neural network, which is different from the previous generation of neural networks. Inspiring by neuroscience, spiking neural network try to build neural networks in a way closer to the human brain mechanism. Referring the application of artificial neural network in image recognition, we decide to use the convolutional neural network in image recognition, moreover, we combine the spiking neural network to construct a new neural network and try to apply it in the field of image classification.
基于脉冲神经网络的图像识别算法
图像识别是计算机视觉的基本任务之一,也是机器学习领域的重要研究方向之一。人工神经网络算法在图像识别方面取得了非常显著的成绩,卷积神经网络是目前最流行的人工神经网络之一,也是目前图像识别的主要解决方案。脉冲神经网络与前一代神经网络不同,被称为第三代神经网络。受神经科学的启发,尖峰神经网络试图以更接近人类大脑机制的方式构建神经网络。参考人工神经网络在图像识别中的应用,我们决定将卷积神经网络应用于图像识别,并结合峰值神经网络构建新的神经网络,并尝试将其应用于图像分类领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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