  While deep neural networks take loose inspiration from neuroscience, it is an
open question how seriously to take the analogies between artificial deep
networks and biological neuronal systems. Interestingly, recent work has shown
that deep convolutional neural networks (CNNs) trained on large-scale image
recognition tasks can serve as strikingly good models for predicting the
responses of neurons in visual cortex to visual stimuli, suggesting that
analogies between artificial and biological neural networks may be more than
superficial. However, while CNNs capture key properties of the average
responses of cortical neurons, they fail to explain other properties of these
neurons. For one, CNNs typically require large quantities of labeled input data
for training. Our own brains, in contrast, rarely have access to this kind of
supervision, so to the extent that representations are similar between CNNs and
brains, this similarity must arise via different training paths. In addition,
neurons in visual cortex produce complex time-varying responses even to static
inputs, and they dynamically tune themselves to temporal regularities in the
visual environment. We argue that these differences are clues to fundamental
differences between the computations performed in the brain and in deep
networks. To begin to close the gap, here we study the emergent properties of a
previously-described recurrent generative network that is trained to predict
future video frames in a self-supervised manner. Remarkably, the model is able
to capture a wide variety of seemingly disparate phenomena observed in visual
cortex, ranging from single unit response dynamics to complex perceptual motion
illusions. These results suggest potentially deep connections between recurrent
predictive neural network models and the brain, providing new leads that can
enrich both fields.
