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Distinguishing theory from implementation in predictive coding accounts of brain function

Published online by Cambridge University Press:  10 May 2013

Michael W. Spratling
Michael W. Spratling*
Affiliation:
Department of Informatics, King's College London, University of London, London WC2R 2LS, United Kingdom. [email protected]
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Abstract

It is often helpful to distinguish between a theory (Marr's computational level) and a specific implementation of that theory (Marr's physical level). However, in the target article, a single implementation of predictive coding is presented as if this were the theory of predictive coding itself. Other implementations of predictive coding have been formulated which can explain additional neurobiological phenomena.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 231 - 232
Copyright
Copyright © Cambridge University Press 2013 

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References

Bechtel, W. (2006) Reducing psychology while maintaining its autonomy via mechanistic explanation. In: The matter of the mind: Philosophical essays on psychology, neuroscience and reduction, ed. Schouten, M. & de Jong, H. L., Ch. 8. Blackwell.Google Scholar
Carandini, M. (2012) From circuits to behavior: A bridge too far? Nature Neuroscience 15(4):507–509.Google Scholar
Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., Gallant, J. L. & Rust, N. C. (2005) Do we know what the early visual system does? Journal of Neuroscience 25(46):10577–97.Google Scholar
Feldman, H. & Friston, K. J. (2010) Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4:215. doi:10.3389/fnmuh.2010.00215.Google Scholar
Friston, K. (2005) A theory of cortical responses. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360(1456):815–36.Google Scholar
Lochmann, T. & Deneve, S. (2011) Neural processing as causal inference. Current Opinion in Neurobiology 21(5):774–78.Google Scholar
Mareschal, D., Johnson, M. H., Siros, S., Spratling, M. W., Thomas, M. S. C. & Westermann, G. (2007) Neuroconstructivism – I.: How the brain constructs cognition. Oxford University Press.CrossRefGoogle Scholar
Olshausen, B. A. & Field, D. J. (2005) How close are we to understanding V1? Neural Computation 17:1665–99.Google Scholar
Phillips, W. A. & Singer, W. (1997) In search of common foundations for cortical computation. Behavioral and Brain Sciences 20:657–722.Google Scholar
Rao, R. P. N. & Ballard, D. H. (1999) Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2(1):79–87.Google Scholar
Spratling, M. W. (2008a) Predictive coding as a model of biased competition in visual attention. Vision Research 48(12):1391–408.Google Scholar
Spratling, M. W. (2008b) Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience 2(4):1–8.CrossRefGoogle ScholarPubMed
Spratling, M. W. (2010) Predictive coding as a model of response properties in cortical area V1. Journal of Neuroscience 30(9):3531–543.Google Scholar
Spratling, M. W. (2011) A single functional model accounts for the distinct properties of suppression in cortical area V1. Vision Research 51(6):563–76.Google Scholar
Spratling, M. W. (2012a) Predictive coding accounts for V1 response properties recorded using reverse correlation. Biological Cybernetics 106(1):37–49.CrossRefGoogle ScholarPubMed
Spratling, M. W. (2012b) Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function. Neural Computation 24(1):60–103.CrossRefGoogle Scholar