  Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on many visual tasks. Recently, they became useful models for the
visual system in neuroscience. However, it is still not clear what are learned
by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used
for the visual system, it is not easy to compare the structure components of
CNNs with possible neuroscience underpinnings 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 biophysical models and recording data
from animals. By training CNNs with white noise images to predict neuronal
responses, we found that fine structures of the retinal receptive field can be
revealed. Specifically, convolutional filters learned are resembling biological
components of the retinal circuit. This suggests that a CNN learning from one
single retinal cell reveals a minimal neural network carried out in this cell.
Furthermore, when CNNs learned from different cells are transferred between
cells, there is a diversity of transfer learning performance, which indicates
that CNNs are cell-specific. Moreover, when CNNs are transferred between
different types of input images, here white noise v.s. natural images, transfer
learning shows a good performance, which implies that CNNs indeed capture the
full computational ability of a single retinal cell for different inputs. Taken
together, these results suggest that CNNs could be used to reveal structure
components of neuronal circuits, and provide a powerful model for neural system
identification.
