# PreCNet: Next-Frame Video Prediction Based on Predictive Coding

arXiv: https://arxiv.org/abs/2004.14878 · HTML: https://mlsanity.com/paper/2004.14878/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images](https://mlsanity.com/paper/2208.09463/) (2022)
- [Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction](https://mlsanity.com/paper/2212.11642/) (2022)
- [Anti-aliasing Predictive Coding Network for Future Video Frame Prediction](https://mlsanity.com/paper/2301.05421/) (2023)
- [Benefits of Feature Extraction and Temporal Sequence Analysis for Video Frame Prediction: An Evaluation of Hybrid Deep Learning Models](https://mlsanity.com/paper/2508.00898/) (2025)

## Abstract (arXiv)

Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao and Ballard (1999) into a modern deep learning framework while remaining maximally faithful to the original schema. The resulting network we propose (PreCNet) is tested on a widely used next frame video prediction benchmark, which consists of images from an urban environment recorded from a car-mounted camera, and achieves state-of-the-art performance. Performance on all measures (MSE, PSNR, SSIM) was further improved when a larger training set (2M images from BDD100k), pointing to the limitations of the KITTI training set. This work demonstrates that an architecture carefully based in a neuroscience model, without being explicitly tailored to the task at hand, can exhibit exceptional performance.