  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.
