  A central challenge in neuroscience is to understand neural computations and
circuit mechanisms that underlie the encoding of ethologically relevant,
natural stimuli. In multilayered neural circuits, nonlinear processes such as
synaptic transmission and spiking dynamics present a significant obstacle to
the creation of accurate computational models of responses to natural stimuli.
Here we demonstrate that deep convolutional neural networks (CNNs) capture
retinal responses to natural scenes nearly to within the variability of a
cell's response, and are markedly more accurate than linear-nonlinear (LN)
models and Generalized Linear Models (GLMs). Moreover, we find two additional
surprising properties of CNNs: they are less susceptible to overfitting than
their LN counterparts when trained on small amounts of data, and generalize
better when tested on stimuli drawn from a different distribution (e.g. between
natural scenes and white noise). Examination of trained CNNs reveals several
properties. First, a richer set of feature maps is necessary for predicting the
responses to natural scenes compared to white noise. Second, temporally precise
responses to slowly varying inputs originate from feedforward inhibition,
similar to known retinal mechanisms. Third, the injection of latent noise
sources in intermediate layers enables our model to capture the sub-Poisson
spiking variability observed in retinal ganglion cells. Fourth, augmenting our
CNNs with recurrent lateral connections enables them to capture contrast
adaptation as an emergent property of accurately describing retinal responses
to natural scenes. These methods can be readily generalized to other sensory
modalities and stimulus ensembles. Overall, this work demonstrates that CNNs
not only accurately capture sensory circuit responses to natural scenes, but
also yield information about the circuit's internal structure and function.
