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Deep convolutional neural networks (CNNs) have demonstrated impressiveperformance on many visual tasks. Recently, they became useful models for thevisual system in neuroscience. However, it is still not clear what are learnedby CNNs in terms of neuronal circuits. When a deep CNN with many layers is usedfor the visual system, it is not easy to compare the structure components ofCNNs with possible neuroscience underpinnings due to highly complex circuitsfrom the retina to higher visual cortex. Here we address this issue by focusingon single retinal ganglion cells with biophysical models and recording datafrom animals. By training CNNs with white noise images to predict neuronalresponses, we found that fine structures of the retinal receptive field can berevealed. Specifically, convolutional filters learned are resembling biologicalcomponents of the retinal circuit. This suggests that a CNN learning from onesingle retinal cell reveals a minimal neural network carried out in this cell.Furthermore, when CNNs learned from different cells are transferred betweencells, there is a diversity of transfer learning performance, which indicatesthat CNNs are cell-specific. Moreover, when CNNs are transferred betweendifferent types of input images, here white noise v.s. natural images, transferlearning shows a good performance, which implies that CNNs indeed capture thefull computational ability of a single retinal cell for different inputs. Takentogether, these results suggest that CNNs could be used to reveal structurecomponents of neuronal circuits, and provide a powerful model for neural systemidentification.

Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks