  Representational straightening refers to a decrease in curvature of visual
feature representations of a sequence of frames taken from natural movies.
Prior work established straightening in neural representations of the primate
primary visual cortex (V1) and perceptual straightening in human behavior as a
hallmark of biological vision in contrast to artificial feedforward neural
networks which did not demonstrate this phenomenon as they were not explicitly
optimized to produce temporally predictable movie representations. Here, we
show robustness to noise in the input image can produce representational
straightening in feedforward neural networks. Both adversarial training (AT)
and base classifiers for Random Smoothing (RS) induced remarkably straightened
feature codes. Demonstrating their utility within the domain of natural movies,
these codes could be inverted to generate intervening movie frames by linear
interpolation in the feature space even though they were not trained on these
trajectories. Demonstrating their biological utility, we found that AT and RS
training improved predictions of neural data in primate V1 over baseline models
providing a parsimonious, bio-plausible mechanism -- noise in the sensory input
stages -- for generating representations in early visual cortex. Finally, we
compared the geometric properties of frame representations in these networks to
better understand how they produced representations that mimicked the
straightening phenomenon from biology. Overall, this work elucidating emergent
properties of robust neural networks demonstrates that it is not necessary to
utilize predictive objectives or train directly on natural movie statistics to
achieve models supporting straightened movie representations similar to human
perception that also predict V1 neural responses.
