Introduction to Predictive Coding Networks for Machine Learning

TL;DR

This paper introduces predictive coding networks (PCNs), a biologically inspired framework for hierarchical computation, providing foundational understanding, implementation details, and demonstrating their application on CIFAR-10 with a Python example.

Contribution

It offers an accessible introduction to PCNs for ML practitioners, including architecture, inference, learning rules, and a practical image classification benchmark.

Findings

Successful application of PCNs on CIFAR-10 dataset

Provides a PyTorch implementation for reproducibility

Highlights PCNs as an alternative to traditional neural networks

Abstract

Predictive coding networks (PCNs) constitute a biologically inspired framework for understanding hierarchical computation in the brain, and offer an alternative to traditional feedforward neural networks in ML. This note serves as a quick, onboarding introduction to PCNs for machine learning practitioners. We cover the foundational network architecture, inference and learning update rules, and algorithmic implementation. A concrete image-classification task (CIFAR-10) is provided as a benchmark-smashing application, together with an accompanying Python notebook containing the PyTorch implementation.