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Predictions in the light of your own action repertoire as a general computational principle

Published online by Cambridge University Press:  10 May 2013

Peter König
Affiliation:
Institute of Cognitive Science, University Osnabrück, 49076 Osnabrück, Germany. [email protected]://cogsci.uni-osnabrueck.de/~NBP/[email protected]://cogsci.uni-osnabrueck.de/~nwilming/[email protected]://kai-kaspar.jimdo.com/[email protected]://cogsci.uni-osnabrueck.de/en/changingbrains/people/saskia Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
Niklas Wilming
Affiliation:
Institute of Cognitive Science, University Osnabrück, 49076 Osnabrück, Germany. [email protected]://cogsci.uni-osnabrueck.de/~NBP/[email protected]://cogsci.uni-osnabrueck.de/~nwilming/[email protected]://kai-kaspar.jimdo.com/[email protected]://cogsci.uni-osnabrueck.de/en/changingbrains/people/saskia
Kai Kaspar
Affiliation:
Institute of Cognitive Science, University Osnabrück, 49076 Osnabrück, Germany. [email protected]://cogsci.uni-osnabrueck.de/~NBP/[email protected]://cogsci.uni-osnabrueck.de/~nwilming/[email protected]://kai-kaspar.jimdo.com/[email protected]://cogsci.uni-osnabrueck.de/en/changingbrains/people/saskia
Saskia K. Nagel
Affiliation:
Institute of Cognitive Science, University Osnabrück, 49076 Osnabrück, Germany. [email protected]://cogsci.uni-osnabrueck.de/~NBP/[email protected]://cogsci.uni-osnabrueck.de/~nwilming/[email protected]://kai-kaspar.jimdo.com/[email protected]://cogsci.uni-osnabrueck.de/en/changingbrains/people/saskia
Selim Onat
Affiliation:
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany. [email protected]

Abstract

We argue that brains generate predictions only within the constraints of the action repertoire. This makes the computational complexity tractable and fosters a step-by-step parallel development of sensory and motor systems. Hence, it is more of a benefit than a literal constraint and may serve as a universal normative principle to understand sensorimotor coupling and interactions with the world.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2013 

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References

Barlow, H. B. (1961) Possible principles underlying the transformations of sensory messages. In: Sensory communication, ed. Rosenblith, W., pp. 217–34. (Chapter 13). MIT Press.Google Scholar
Berkes, P. & Wiskott, L. (2005) Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision 5(6):579602.Google Scholar
Betsch, B. Y., Einhäuser, W., Körding, K. P. & König, P. (2004) The world from a cat's perspective – statistics of natural videos. Biological Cybernetics 90:4150.CrossRefGoogle ScholarPubMed
Einhäuser, W., Kayser, C., König, P. & Körding, K. P. (2002) Learning the invariance properties of complex cells from their responses to natural stimuli. European Journal of Neuroscience 15:475–86.Google Scholar
Einhäuser, W., Moeller, G. U., Schumann, F., Conradt, J., Vockeroth, J., Bartl, K., Schneider, E. & König, P. (2009) Eye-head coordination during free exploration in human and cat. Annals of the New York Academy of Sciences 1164:353–66.Google Scholar
Friston, K. J. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38.CrossRefGoogle ScholarPubMed
Kärcher, S. M., Fenzlaff, S., Hartmann, D., Nagel, S. K. & König, P. (2012) Sensory augmentation for the blind. Frontiers in Human Neuroscience 6:37.CrossRefGoogle ScholarPubMed
König, P. & Krüger, N. (2006) Symbols as self-emergent entities in an optimization process of feature extraction and predictions. Biological Cybernetics 94(4):325–34.Google Scholar
Körding, K. P., Kayser, C., Einhäuser, W. & König, P. (2004) How are complex cell properties adapted to the statistics of natural stimuli? Journal of Neurophysiology 91(1):206–12.Google Scholar
Nagel, S. K., Carl, C., Kringe, T., Märtin, R. & König, P. (2005) Beyond sensory substitution – learning the sixth sense. Journal of Neural Engineering 2(4):R13R26. doi:10.1088/1741-2560/2/4/R02.Google Scholar
Olshausen, B. A. & Field, D. J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607609.Google Scholar
Phillips, W. A., Kay, J. & Smyth, D. (1995) The discovery of structure by multistream networks of local processors with contextual guidance. Network: Computation in Neural Systems 6:225–46.CrossRefGoogle Scholar
Piaget, J. (1952) The origins of intelligence in children. International University Press.Google Scholar
Segall, M. H., Campbell, D. T. & Herskovits, M. J. (1963) Cultural differences in the perception of geometric illusions. Science 139(3556):769–71.Google Scholar
Simoncelli, E. P. & Olshausen, B. A. (2001) Natural image statistics and neural representation. Annual Review of Neuroscience 24:1193–216.Google Scholar
Tanaka, K. (1996) Inferotemporal cortex and object vision. Annual Review of Neuroscience 19:109–39.Google Scholar
Wyss, R., König, P. & Verschure, P. F. M. J. (2004) Involving the motor system in decision making. Proceedings of the Royal Society of London, B: Biological Sciences 271(Suppl. 3):S5052.CrossRefGoogle ScholarPubMed