Abstract: Focused on the issue of decoding visual input from neuron response signal, a method to reconstruct visual input using neurons action potential (Spike) signal was proposed. Firstly, the Spike signal from the pigeon Optic Tectum (OT) neurons was recorded and the firing rate characteristics of Spike were extracted. Then, a linear inverse filter reconstruction model and a convolution neural network reconstruction model were constructed to realize the reconstruction of the visual input. Finally, the number of channels, time bin, data time length and delay time were optimized. Under the same parameter condition, the cross correlation coefficient of image reconstruction using linear inverse filter reconstruction model reached 0.910 7±0.021 9, and the cross correlation coefficient of image reconstruction using convolution neural network reconstruction model reached 0.927 1±0.017 6. The results show that the visual input can be reconstructed effectively by extracting firing rate characteristics of neuron Spike and using linear inverse filter reconstruction model and convolution neural network reconstruction model.

Key words: optic tectum, Spike, firing rate, image reconstruction, cross correlation coefficient

摘要： 针对从神经元响应信号中解码视觉输入的问题，提出了一种利用神经元动作电位（Spike）信号重建视觉输入的方法。首先，记录鸽视顶盖（OT）神经元的Spike信号，提取Spike发放率特征；然后，构建线性逆滤波器和卷积神经网络重建模型，实现视觉输入的重建；最后，对通道数量、时间窗口、数据时间长度、延迟时间等参数进行优化。在相同参数条件下，利用线性逆滤波器重建图像的互相关系数达到0.910 7±0.021 9，利用卷积神经网络模型重建图像的互相关系数达到0.927 1±0.017 6。重建结果表明，提取神经元Spike发放率特征并运用线性逆滤波器和卷积神经网络重建模型可以有效重建视觉输入。

关键词: 视顶盖, 动作电位, 发放率, 图像重建, 互相关系数