Predictive Coding for Deep Neural Networks

Summary

We review the application of predictive coding - a popular neuroscience framework - to deep neural networks.

Contribution

This work was produced for TIR (Travaux d’Initiation `a la Recherche) course in M1 Computer Science of UPS during year 2021/2022.

Abstract

Predictive Coding is a popular framework in neurosciences for explaining cortical function. In this model, higher-level cortical areas try to predict lower-level neural activity and prediction errors are passed back to higher layers. Deep Neural Networks (DNN) , which use brain-inspired architecture, could be augmented with such a model, providing robustness and a better understanding of spatio-temporal dependencies. We investigate research in this direction and give a quick review on tasks in which Predictive Coding (PC) for DNN has demonstrated its interest, with a strong emphasis on vision-related tasks.

Click here to access the paper

Recommended citation:

@misc{predcoding,
      title={Predictive Coding for Deep Neural Networks}, 
      author={Maxime Chourré, Guilhem Fouilhé, Lison Kardassevitch, Esteban Marco, and Chloé Michel},
      year={2022},
      journal={Travaux d’Initiation à la Recherche, Université Paul Sabatier},
}