基于忆阻器的spiking神经网络在图像边缘提取中的应用

根据生物视觉系统的功能原理,用忆阻器模拟生物突触,结合忆阻器的记忆特性和spiking神经网络的高效处理能力,构造了一种可用于图像边缘提取的三层spiking神经网络模型,该网络用忆阻器电导的变化量来表征图像边缘信息,仿真结果表明,该方法的边缘提取结果具有连续性、光滑性、低误检漏检性和边缘定位准确性,该神经网络的处理过程符合生物信息处理机制,为视觉系统的仿生实现提供了新的思路。

Application of memristor-based spiking neural network in image edge extraction

By simulating biological synapses with memristors according to the function and principle of biological visual system and by combining the memory characteristic of memristor with high-efficient processing ability in spiking neural network, a three-layer spiking neural network model for image edge extraction is constructed, in which the image edge information is represented by the variation of the memristor conductance. The edge extraction result obtained with this approach has the characteristics of continuity, smoothness, low false leak detection and edge positioning accuracy. Since the processing mechanism of this neural network conforms to the biological counterpart, it offers a new idea for the bionic implementation of biological visual system.
Keywords： spiking neural network memristor synapse image edge extraction