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Predictive Coding: a Theoretical and Experimental Review
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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.
Forward citations
Cited by 5 Pith papers
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Introduction to Predictive Coding Networks for Machine Learning
The paper derives standard predictive coding update rules and claims a 99.92% CIFAR-10 accuracy that would beat the published leaderboard, but the claim is unverified and internally inconsistent.
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Bridging Predictive Coding and MDL: A Two-Part Code Framework for Deep Learning
A theoretical framework claims that predictive coding performs block-coordinate descent on a two-part code objective and bounds true risk by empirical risk plus codelength divided by sample size.
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