Wang Chaoming, Lian Risheng, Dong Xingsi, Mi Yuanyuan, Wu Si
Peking-Tsinghua Center for Life Sciences, School of Electronics Engineering and Computer Science, IDG/McGovern Institute for Brain Research, Peking University, Academy for Advanced Interdisceplinary Studies, Beijing, China.
Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China.
Front Comput Neurosci. 2020 Oct 14;14:571982. doi: 10.3389/fncom.2020.571982. eCollection 2020.
Visual information processing in the brain goes from global to local. A large volume of experimental studies has suggested that among global features, the brain perceives the topological information of an image first. Here, we propose a neural network model to elucidate the underlying computational mechanism. The model consists of two parts. The first part is a neural network in which neurons are coupled through gap junctions, mimicking the neural circuit formed by alpha ganglion cells in the retina. Gap junction plays a key role in the model, which, on one hand, facilitates the synchronized firing of a neuron group covering a connected region of an image, and on the other hand, staggers the firing moments of different neuron groups covering disconnected regions of the image. These two properties endow the network with the capacity of detecting the connectivity and closure of images. The second part of the model is a read-out neuron, which reads out the topological information that has been converted into the number of synchronized firings in the retina network. Our model provides a simple yet effective mechanism for the neural system to detect the topological information of images in ultra-speed.
大脑中的视觉信息处理是从全局到局部的。大量实验研究表明，在全局特征中，大脑首先感知图像的拓扑信息。在此，我们提出一种神经网络模型来阐明其潜在的计算机制。该模型由两部分组成。第一部分是一个神经网络，其中神经元通过缝隙连接耦合，模仿视网膜中α神经节细胞形成的神经回路。缝隙连接在模型中起关键作用，一方面，它促进覆盖图像连通区域的神经元群同步放电，另一方面，它错开覆盖图像不连通区域的不同神经元群的放电时刻。这两个特性赋予网络检测图像连通性和闭合性的能力。模型的第二部分是一个读出神经元，它读出已转换为视网膜网络中同步放电次数的拓扑信息。我们的模型为神经系统以超高速检测图像的拓扑信息提供了一种简单而有效的机制。
