# PredNet and Predictive Coding: A Critical Review

## TL;DR

This paper critically evaluates PredNet, a deep predictive coding network for video prediction, revealing its limitations in following predictive coding principles and assessing the impact of top-down conditioning on real-world data.

## Contribution

It provides a critical analysis of PredNet's adherence to predictive coding theory and explores the effects of top-down conditioning on model performance in complex datasets.

## Key findings

- PredNet does not fully follow predictive coding principles.
- Top-down conditioning improves performance on synthetic data.
- Performance gains do not scale to real-world datasets.

## Abstract

PredNet, a deep predictive coding network developed by Lotter et al., combines a biologically inspired architecture based on the propagation of prediction error with self-supervised representation learning in video. While the architecture has drawn a lot of attention and various extensions of the model exist, there is a lack of a critical analysis. We fill in the gap by evaluating PredNet both as an implementation of the predictive coding theory and as a self-supervised video prediction model using a challenging video action classification dataset. We design an extended model to test if conditioning future frame predictions on the action class of the video improves the model performance. We show that PredNet does not yet completely follow the principles of predictive coding. The proposed top-down conditioning leads to a performance gain on synthetic data, but does not scale up to the more complex real-world action classification dataset. Our analysis is aimed at guiding future research on similar architectures based on the predictive coding theory.