Abstract
What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.
Abstract (translated)
我们可以从connectome中学到什么？我们构建了苍蝇视觉系统前两个阶段的简化模型，即椎板和髓质。由此产生的六角格子卷积网络通过反向传播训练以在自然场景视频中执行对象跟踪。初始化来自connectome重建的权重的网络自动发现了T4神经元及其输入中众所周知的定向和方向选择性属性，而随机初始化的网络则没有。我们的工作是第一次演示，connectome的知识可以在电路上预测电路中单个神经元的功能特性，从而单独从结构中理解电路功能。
URL
https://arxiv.org/abs/1806.04793
