Reconciling Predictive Coding and Biased Competition Models of Cortical Function

Michael W Spratling

doi:10.3389/neuro.10.004.2008

Michael W Spratling

Open Access

https://doi.org/10.3389/neuro.10.004.2008

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Abstract

A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.

Highlights

Predictive coding (Jehee et al, 2006; Rao and Ballard, 1999) and biased competition (Desimone and Duncan, 1995; Reynolds et al, 1999) are two highly influential theories of cortical visual information processing. Both theories propose that perception involves the interaction between top-down expectation and sensory-driven analysis.,predictive coding hypothesizes that cortical feedback connections act to suppress information predicted by higher-level cortical regions, so that only the residual error between the top-down prediction and the bottom-up input is propagated from one cortical region to the along a processing pathway
At first sight the biased competition and predictive coding theories seem to be diametrically opposed: one requires cortical feedback to be excitatory while the other proposes that feedback is suppressive
The predictive coding and biased competition models have been considered as distinct theories of cortical function

Summary

Introduction

Predictive coding (Jehee et al, 2006; Rao and Ballard, 1999) and biased competition (Desimone and Duncan, 1995; Reynolds et al, 1999) are two highly influential theories of cortical visual information processing. Biased competition proposes that cortical feedback acts to enhance stimulus-driven neural activity that is consistent with top-down predictions in order to affect competition occurring between neural representations in each cortical area. These two theories are presumed to be incompatible and to make a number of rival predictions. In this article it is demonstrated that predictive coding is mathematically equivalent to a particular form of biased competition model in which the nodes compete via negative feedback (Harpur and Prager, 1994, 1996; Spratling and Johnson, 2004). The neural architecture derived from the biased competition model seems most consistent with cortical physiology

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Discussion

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