基于脉冲神经网络的时空交互图像分类

Spatio-temporal Interactive Image Classification Based on Spiking Neural Network

脉冲神经网络作为人工智能发展的重要方向之一,在神经形态工程和类脑计算领域得到了广泛的关注.为解决脉冲神经网络泛化性差、内存和时间消耗较大等问题,本文提出了一种基于脉冲神经网络的时空交互图像分类方法.首先引入时间有效训练算法弥补梯度下降过程中的动能损失;其次融合空间随时间学习算法,提高网络对信息的高效处理能力;最后添加空间注意力机制,增强网络对空间维度上重要特征的捕捉能力.实验结果表明,改进后的方法在CIFAR10、DVS Gesture、CIFAR10-DVS这3个数据集上的训练内存占用分别减少了46.68%、48.52%、10.46%,训练速度分别提升了2.80倍、1.31倍、2.76倍,在保证精度的情况下,网络性能得到有效提升.

As one of the important development directions of artificial intelligence,spiking neural networks have received extensive attention in the fields of neuromorphic engineering and brain-inspired computing.To solve the problems of poor generalization as well as large memory and time consumption in spiking neural networks,this study proposes a classification method based on spiking neural networks for spatio-temporal interactive images.Specifically,a temporal efficient training algorithm is introduced to compensate for the kinetic energy loss in the gradient descent process.Then,the spatial learning through time algorithms are integrated to improve the ability of the network to process information efficiently.Finally,the spatial attention mechanism is added to enable the network to better capture important features in the spatial dimension.The experimental results show that the training memory occupation on the three datasets of CIFAR10,DVS Gesture,and CIFAR10-DVS are reduced by 46.68%,48.52%,and 10.46%,respectively,and the training speed is increased by 2.80 times,1.31 times,and 2.76 times,respectively.These results indicate that the proposed method improves network performance effectively on the premise of maintaining accuracy.