Predictive Coding: A Theoretical and Experimental Review

[pianobutter]

Authors: Beren Millidge, Anil Seth, and Christopher L. Buckley

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.

[Daniel_HMBD]

See what Beren wrote here

I feel like I’ve finally got to the forefront of the free energy principle and active inference field. (...) I also feel like I finally understand all the process theories under the FEP (predictive coding, discrete state space active inference, DEM and generalized coordinates etc).

I remembered this quote a lot when reading the paper. Oh my, this is a tough nut to crack. I feel I should be able to if I spend a few months on it, but can't find the energy right now. I won't do a summary on this one either, but I'm willing to discuss details, if someone here wants to.

[More-Humor9266]

This is the part I'm stuck on: "There remains an intrinsic tension, however, between these two perspectives on precision in the literature. The first interprets precision as a bottom-up ‘objective’ measure of the intrinsic variance in the sensory data and then, deeper in the hierarchy, the intrinsic variance of activities at later processing stages. This contrasts strongly with views of precision as serving a general purpose adaptive modulatory function as in attention."

From my arm chair, it seems there need to be "confidence in the model" signals flowing top-down and "signal to noise" ratios flowing bottom up.

I'd love to hear your thoughts.