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With or without you: predictive coding and Bayesian inference in the brain

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Aitchison, L 

Abstract

Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic / representational motif that can serve several different computational goals of which Bayesian inference is but one. Conversely, while Bayesian inference can utilize predictive coding, it can also be realized by a variety of other representations. We critically evaluate the experimental evidence supporting Bayesian predictive coding and discuss how to test it more directly.

Description

Keywords

Algorithms, Animals, Bayes Theorem, Brain, Computer Simulation, Humans, Models, Neurological

Journal Title

Current Opinion in Neurobiology

Conference Name

Journal ISSN

0959-4388
1873-6882

Volume Title

46

Publisher

Elsevier
Sponsorship
Wellcome Trust (095621/Z/11/Z)
This work was supported by the Wellcome Trust.