Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.

翻译：本文在Galaxy Zoo数据集上实现了通过模仿人体体感系统构建的逐步输入深度神经网络——SpinalNet。SpinalNet中的输入分段技术使中间层能够同时接收部分输入及前层输出，从而减少了中间层的权重参数总量。SpinalNet的作者报告称，在测试的大多数深度神经网络中，该方法不仅显著降低了误差，还大幅削减了计算开销。通过将其应用于Galaxy Zoo数据集，我们能够对星系的不同类别和/或子类进行分类。最终在椭圆星系与旋涡星系、这两类与不规则星系、以及十类星系子类之间分别获得了98.2%、95%和82%的高分类准确率。