Traditionally, convolutional neural networks are feedforward networks with a deep and complex hierarchy. Conversely, the human brain has a relatively shallow hierarchy with recurrent connections. Replicating this recurrence may allow for shallower and easier to understand computer vision models that may possess characteristics usually attributed to the brain. One of the characteristics we examine in this paper is the effect of recurrence on the robustness of the model against noise. Additionally, we vary the type of noise in order to observe what behaviour is universal and what behaviour is specific to the noise. However, it is crucial to make the distinction that recurrence in the brain is different from that in classical recurrent models. The key difference lies in how information passes through the layers at every step. This gap has been addressed by CORnet, a family of models that use a more biologically compatible hierarchy of recurrence.

In this paper, we take the idea of the biologically compatible recurrent hierarchy and look at the effects of recurrence when applied to a baseline down-scaled Resnet model. We show different settings where recurrence results in an increase in adversarial robustness, and settings where recurrence has the opposite effect. To offer an additional perspective of robustness, we also show how recurrence makes the feature maps of a model more resilient to perturbations. Following these observations, we conclude that biological recurrence allows the model to average out white noise spatio-temporally and we use the observations to support our hypothesis.