Predictive Coding: a Theoretical and Experimental Review

TL;DR

This paper provides a comprehensive review of predictive coding theory, covering its mathematical foundations, neurobiological plausibility, and connections to machine learning, highlighting recent developments and empirical evidence.

Contribution

It offers the first extensive synthesis of predictive coding's core mathematical structure, biological implementation, and links to machine learning, updating the field's understanding.

Findings

Predictive coding aligns with Bayesian brain principles.

Neurobiological microcircuits may implement predictive coding.

Connections between predictive coding and backpropagation are explored.

Abstract

Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience. A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory. Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.