Wang Zhizhong, Jiao Xingyang, Wang Songwei, Niu Xiaoke, Shi Li
Department of Automation, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Neuroreport. 2018 Sep 5;29(13):1092-1098. doi: 10.1097/WNR.0000000000001077.
Reconstruction of visual input through a neuron response helps to understand the information processing mechanism of the visual system. This paper uses the amplitude and phase characteristics of the local field potential signal in the pigeon optic tectum area to reconstruct the visual input from the neuron response data by means of local information accumulation using a linear inverse filter and a back propagation neural network algorithm. The reconstructed results show that the correlation between three reconstructed images and their corresponding stimulus images (tree branches, birds, and eyeglasses) was 0.8461±0.1135 for optimal values of number of channels, response duration, time from stimulus onset, and frequency band. This method of reconstructing the natural image from the pigeon optic tectum area neuron response signal can be applied to coding mechanism analysis of brightness and structural information in the visual system and to feedback from implantable visual prostheses.
通过神经元反应重建视觉输入有助于理解视觉系统的信息处理机制。本文利用鸽子视顶盖区域局部场电位信号的幅度和相位特征,借助线性逆滤波器和反向传播神经网络算法,通过局部信息累积从神经元反应数据中重建视觉输入。重建结果表明,对于通道数、反应持续时间、刺激开始时间和频带的最佳值,三张重建图像与其相应刺激图像(树枝、鸟类和眼镜)之间的相关性为0.8461±0.1135。这种从鸽子视顶盖区域神经元反应信号重建自然图像的方法可应用于视觉系统中亮度和结构信息的编码机制分析以及植入式视觉假体的反馈。