Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T14:10:06.119Z Has data issue: false hasContentIssue false
  • English
  • Français

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 ,
Niklas Wilming ,
Kai Kaspar ,
Saskia K. Nagel  and
Selim Onat
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]
Get access Rights & Permissions [Opens in a new window]

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
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 219 - 220
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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):579–602.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 90:41–50.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):R13–R26. 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):607–609.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):S50–52.CrossRefGoogle ScholarPubMed