Free-energy and the brain |
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Authors: | Karl J Friston Klaas E Stephan |
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Institution: | (1) Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, UK |
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Abstract: | If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual
inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics
it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium
can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible
fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The
use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This
scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest
that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy
considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded
by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples
the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead
to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the
system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed
by the brain and how minimisation of free-energy can explain its dynamics and structure. |
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Keywords: | Variational Bayes Free-energy Inference Perception Action Value Learning Attention Selection Hierarchical |
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