Original Research Article
Reconciling predictive coding and biased competition models of cortical function
Michael W. Spratling 1, 2*
1 Division of Engineering, King's College London, UK
2 Centre for Brain and Cognitive Development, University of London, UK
2 Centre for Brain and Cognitive Development, University of London, UK
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.
Keywords: neural networks, cortical circuits, cortical feedback, biased competition, predictive coding
Copyright: © 2008 Spratling. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence: M. W. Spratling, Division of Engineering, King’s College London, Strand, London, WC2R 2LS, UK. michael.spratling@kcl.ac.uk
Citation: Spratling MW (2008) Reconciling predictive coding and biased competition models of cortical function. Front. Comput. Neurosci. (2008) 2:4. doi:10.3389/neuro.10.004.2008
Received: 23 June 2008; paper pending published: 10 September 2008; accepted: 09 October 2008; published online: 21 October 2008.
Edited by:
Klaus R. Pawelzik, University of Bremen, Germany
Reviewed by:
Klaus H. Obermayer, Technical University of Berlin, Germany
Jochen Triesch, Johann Wolfgang Goethe University, Germany
Jochen Triesch, Johann Wolfgang Goethe University, Germany
*Correspondence: M. W. Spratling, Division of Engineering, King’s College London, Strand, London, WC2R 2LS, UK. michael.spratling@kcl.ac.uk
