  Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on visual object classification tasks. In addition, it is a useful
model for predication of neuronal responses recorded in visual system. However,
there is still no clear understanding of what CNNs learn in terms of visual
neuronal circuits. Visualizing CNN's features to obtain possible connections to
neuronscience underpinnings is not easy due to highly complex circuits from the
retina to higher visual cortex. Here we address this issue by focusing on
single retinal ganglion cells with a simple model and electrophysiological
recordings from salamanders. By training CNNs with white noise images to
predicate neural responses, we found that convolutional filters learned in the
end are resembling to biological components of the retinal circuit. Features
represented by these filters tile the space of conventional receptive field of
retinal ganglion cells. These results suggest that CNN could be used to reveal
structure components of neuronal circuits.
