Free-energy and the brain
Open Access
- 5 September 2007
- journal article
- research article
- Published by Springer Science and Business Media LLC in Synthese
- Vol. 159 (3), 417-458
- https://doi.org/10.1007/s11229-007-9237-y
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.Keywords
This publication has 77 references indexed in Scilit:
- Bayesian surprise attracts human attentionVision Research, 2008
- Hierarchical Bayesian inference in the visual cortexJournal of the Optical Society of America A, 2003
- The computational modeling of nonequilibrium thermodynamics of the martensitic transformationsComputational Mechanics, 1998
- On the actions that one nerve cell can have on another: Distinguishing “drivers” from “modulators”Proceedings of the National Academy of Sciences of the United States of America, 1998
- A forward-inverse optics model of reciprocal connections between visual cortical areasNetwork: Computation in Neural Systems, 1993
- Laminar distribution of NMDA receptors in cat and monkey visual cortex visualized by [3H]‐MK‐801 bindingJournal of Comparative Neurology, 1993
- Towards a Theory of Early Visual ProcessingNeural Computation, 1990
- Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)Journal of the American Statistical Association, 1989
- Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkeyBrain Research, 1979
- Maximum Likelihood Approaches to Variance Component Estimation and to Related ProblemsJournal of the American Statistical Association, 1977