Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-26T10:08:37.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Active inference and free energy

Published online by Cambridge University Press:  10 May 2013

Karl Friston
Karl Friston*
Affiliation:
The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, United Kingdom. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 212 - 213
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

Ashby, W. R. (1947) Principles of the self-organizing dynamic system. Journal of General Psychology 37:125–28.Google Scholar
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
Dayan, P., Hinton, G. E. & Neal, R. M. (1995) The Helmholtz machine. Neural Computation 7:889–904.CrossRefGoogle ScholarPubMed
Friston, K. J. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38.CrossRefGoogle ScholarPubMed
Fuster, J. M. (2001) The prefrontal cortex – an update: time is of the essence. Neuron 30:319–33.CrossRefGoogle ScholarPubMed
Gregory, R. L. (1980) Perceptions as hypotheses. Philosophical Transactions of the Royal Society of London B 290(1038):181–97.Google Scholar
Jaynes, E. T. (1957) Information theory and statistical mechanics. Physical Review (Series II) 106(4):620–30.Google Scholar
Mumford, D. (1992) On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biological Cybernetics 66(3):241–51.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
Tribus, M. (1961) Thermodynamics and thermostatics: An introduction to energy, information and states of matter, with engineering applications. D. Van Nostrand.Google Scholar
Ungerleider, L. G. & Mishkin, M. (1982) Two cortical visual systems. In: Analysis of visual behavior, ed. Ingle, D., Goodale, M. A. & Mansfield, R. J., pp. 549–86. MIT Press.Google Scholar
Yuille, A. & Kersten, D. (2006) Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Science 10(7):301–308.Google Scholar