  Deep neural networks excel at image classification, but their performance is
far less robust to input perturbations than human perception. In this work we
explore whether this shortcoming may be partly addressed by incorporating
brain-inspired recurrent dynamics in deep convolutional networks. We take
inspiration from a popular framework in neuroscience: 'predictive coding'. At
each layer of the hierarchical model, generative feedback 'predicts' (i.e.,
reconstructs) the pattern of activity in the previous layer. The reconstruction
errors are used to iteratively update the network's representations across
timesteps, and to optimize the network's feedback weights over the natural
image dataset-a form of unsupervised training. We show that implementing this
strategy into two popular networks, VGG16 and EfficientNetB0, improves their
robustness against various corruptions and adversarial attacks. We hypothesize
that other feedforward networks could similarly benefit from the proposed
framework. To promote research in this direction, we provide an open-sourced
PyTorch-based package called Predify, which can be used to implement and
investigate the impacts of the predictive coding dynamics in any convolutional
neural network.
