The Performance of PredNet Using Predictive Coding in the Visual Cortex: An Empirical Analysis

PredNet is a deep recurrent convolutional neural network developed by Lotter et al.. The architecture drew inspiration from a Hierarchical Neuroscience model of visual processing described and demonstrated by Rao and Ballard. In 2020, Rane, Roshan Prakash, et al. published a critical review of PredNet stating its lack of performance in the task of next frame prediction in videos on a crowd sourced action classification dataset. While their criticism was nearly coherent, it is dubious, when observed, considering the findings reported by Rao and Ballard. In this paper, we reevaluate their review through the application of the two primary datasets used by Lotter et al. and Rane, Roshan Prakash et al.. We address gaps, drawing reasoning using the findings reported by Rao and Ballard. As such, we provide a more comprehensive picture for future research based on predictive coding theory.